Yield comparisons for different crops between hydroponic and open field cultivation.
\r\n\t
",isbn:"978-1-83768-472-4",printIsbn:"978-1-83768-471-7",pdfIsbn:"978-1-83768-473-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"be61949c97a884e4342d41ec7414e678",bookSignature:"Dr. Rahul Shukla",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/12326.jpg",keywords:"Preformulation Studies, Kinetics, Drug Delivery, Analysis, Stability, Drug Content, Optimization, Toxicity, Nanotechnology, Biosensors, Biocompatible, Market Approval",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 24th 2022",dateEndSecondStepPublish:"July 22nd 2022",dateEndThirdStepPublish:"September 20th 2022",dateEndFourthStepPublish:"December 9th 2022",dateEndFifthStepPublish:"February 7th 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"21 days",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Researcher in the fields of Nanomedicine, Particle engineering, nanomaterials, dendrimers for drug delivery, Polymeric nanoparticles, nanocrystals, nanogels, nanoemulsions, and Nano-nutraceuticals for therapeutic applications. Member of Indian Red Cross Society, Association of Pharmaceutical Teachers of India (APTI), Indian Pharmacy Graduate Association.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"319705",title:"Dr.",name:"Rahul",middleName:null,surname:"Shukla",slug:"rahul-shukla",fullName:"Rahul Shukla",profilePictureURL:"https://mts.intechopen.com/storage/users/319705/images/system/319705.jpg",biography:"Currently working as Assistant Professor at Department of Pharmaceutics, NIPER Raebareli, India, did Ph.D. in Pharmaceutical Sciences from CSIR CDRI and J.N.U New Delhi, India, M Pharm from IIT BHU,Varanasi, India and B. Pharm from Jamia Hamdard, New Delhi. He has the past experience of as Research Scientist at Dr Reddys Laboratories, India and D.S Kothari Post-Doctoral Fellow at Panjab University, India. He has more than ten years of research and academic experience. He has more than 100 publications including 40 book chapters to his credit. He has filed 7 Indian Patent and 1 Copyright. Recently he has also published a book in Elsevier,Intech as Editor. His current research interests include targeted drug delivery systems, particle engineering, controlled delivery for neurodegenerative diseases, dendrimer mediated drug delivery, solubilization and bioavailability enhancements.\nEmail id: rahulshuklapharm@gmail.com, rahul.shukla@niperraebareli.edu.in \nhttps://scholar.google.com/citations?hl=en&user=PegtvC0AAAAJ",institutionString:"National Institute of Pharmaceutical Education and Research",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"National Institute of Pharmaceutical Education and Research",institutionURL:null,country:{name:"India"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"19",title:"Pharmacology, Toxicology and Pharmaceutical Science",slug:"pharmacology-toxicology-and-pharmaceutical-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"478197",firstName:"Veronika",lastName:"Radosavac",middleName:null,title:"Dr.",imageUrl:"//cdnintech.com/web/frontend/www/assets/author.svg",email:"veronika@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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Decisive theoretical advances were made by teams led by Professor Fawwaz Ulaby at the University of the Michigan (USA), by Professor Christopher Oliver at the Defence Research Agency in Great Malvern (UK), and by Professor Keith Raney at the Canadian Centre of Remote Sensing (Canada) since the 1970’s. Then, the domain of speckle filtering in SAR images matured in the period 1976-2000, mostly under the impulsion of Dr. Jong Sen Lee of the Naval Research Center, Washington D.C. (USA). Since 1986, the team led by Dr. Armand Lopès at the Centre d\'Etude Spatiale des Rayonnements in Toulouse (France), has carried out and then inspired the development of the most efficient speckle filters existing today. Since 2000, with speckle filters having reached a satisfactory level of performance, no significant advances have been made. Nevertheless, in this period, the use of speckle filters in a wide range of applications using SAR imagery has become generalized.
A radar wave can be considered, with a good approximation, as plane, coherent and monochromatic. It is emitted by an antenna towards a target. The target backscatters partially the radar wave in the direction of a receiving antenna. In the vast majority of spaceborne Synthetic Aperture Radars (SAR), a single antenna assumes the two functions of emission and reception (monostatic radar).
The complete radar measurement is the combination of the horizontally (H) and vertically (V) linearly polarised radar waves, at emission and at reception after backscattering by the observed target. After signal calibration, this measurement, affected by noise, enables to restitute for each resolution cell a polarimetric backscattering matrix
whose coefficients
Let consider the interaction of the radar wave with an extended random surface,
This detected signal intensity
Carrying the radiometric information with regard to the sensed target, σ° is a function of the frequency of the radar wave, of its angle of incidence upon the target, and of the configuration of polarisation. In terms of physical meaning, the radar backscattering coefficient is analogous to the bidirectional reflectance
Nevertheless, detected radar images look visually very noisy, exhibiting a very characteristic salt-and-pepper appearance with strong tonal variations from a pixel to the next. Indeed, since radar imaging systems are time-coherent, radar measurements over random rough surfaces are corrupted by "speckle" noise due to the random modulation of waves reflected by the elementary scatterers randomly located in the resolution cell. Then, coherent summation of the phases of elementary scatterers within the resolution cell results in a random phase of the complex pixel value.
This speckle "noise" makes both photo-interpretation and the estimation of
Therefore, speckle filtering and radar reflectivity restoration are among the main fields of interest in radar images processing for remote sensing. Speckle filtering is a pre-processing aiming at the restoration of
Therefore, an ideal speckle filter must satisfy to the following specifications:
Preserve accurately the local mean value of the radar reflectivity (
Smooth as much as possible homogeneous image areas and therefore reduce the speckle to increase the Equivalent Number of Looks (ENL) of the radar image (
Preserve texture as much as possible where it exists in the image (forests, non-homogeneous fields, etc.) to avoid confusions among radiometrically similar areas exhibiting different texture. Therefore, a speckle filter must be able to discriminate heterogeneity effects due to texture from those due to speckle.
Both preserve and denoise image structures (contours, lines) as well as the quasi-deterministic responses due to corner reflector effects within strongly textured areas such as urban environments. Indeed, the energy of artificial radar reflectors responses must be preserved to enable radiometric calibration, in particular when calibration targets are dispersed in the radar image.
Minimise, and whenever possible prevent loss in useful spatial resolution during the speckle filtering process.
In this section, the statistical properties of speckle in images produced by coherent imaging systems such as imaging radars, lidars or sonars, are exposed. Since a good speckle filter must restore the texture of the scene imaged by the radar, the statistical properties of texture in radar images are examined as well. This analysis intentionally restrains to the first order statistical properties, since only these are generally used by the estimation techniques involved in speckle reduction methods. Explicit use of second order statistical properties of both the speckle and the imaged scene in the filtering process is adressed in Section 4.
First, let consider a natural target with radar backscattering coefficient
The radar imaging system is linear, spatially invariant, and can be characterised at each image pixel (
If the number of individual scatterers within a resolution cell is large enough and none of them has absolute predominance in scattering the radar wave [2] [3], speckle can be modelled in output of the radar processor by a circular complex gaussian random process
These complex data in output of the radar processor are called a "1-look complex" image or equivalently, a "Single-Look-Complex" (SLC) image.
For a homogeneous area where
where
Owing to the particular character of the speckle phenomenon in coherent imagery, which is the case of radar imagery, information extraction from a radar image results in a statistical estimation problem. It is therefore mandatory to have an as complete as possible statistical speckle model. The statistical model of the fully developed speckle has proven perfectly adapted to SAR images, at the spatial resolutions / wavelength frequencies combinations actually used in radar remote sensing. Note that this speckle model emphasises the importance of wave phase information under the condition that the phase has not been lost during signal detection.
Goodman hypotheses [2] [5] enable to calculate speckle statistics before (
where E[.] denotes the mean expectation and
Besides, the in-phase and the out-of-phase components
with a phase of the complex radar signal
Since
It is important to note that, since E[
The two first first-order moments of this pdf can be expressed as a function of its standard-deviation as follows:
To characterise the strength of speckle in radar images, it is convenient to consider the normalised coefficient of variation of the intensity,
The value of the coefficient of variation of speckle only, sometimes called "contrast index", is a constant for every type of radar image product. Equation (9) means that, in a radar image, the dispersion (variance) of radiometry increases as the mean signal backscattered by the target increases. This justifies in part the qualification of "multiplicative noise" given to the speckle.
Clearly, with a signal-to-noise ratio of 1, the radiometric degradation due to speckle makes very difficult the discrimination of two homogeneous targets with very different radar backscattering coefficients. As an example, a theoretical computation [10] demonstrates that two textureless target classes of homogeneous radar reflectivities (
Therefore, a first radiometric enhancement is needed to achieve a reduction of the coefficient of variation of the speckle over homogeneous areas. It corresponds to an enhancement of the signal-to-noise ratio and to a preliminary speckle "noise" reduction.
A first method of speckle reduction consists in averaging incoherently
The goal of this method is to reduce speckle enough to make radar image photo-interpretation possible. Indeed, experience has shown that
This operation is realised by splitting the Doppler bandwidth of the backscattered signal into
Over the same target, the mean intensity resulting from this operation remains the same mean intensity of each of the individual looks. If the
It is important to note that multilook radar image formation is at the expense of the spatial resolution in azimuth. In practice, the value of
If the individual looks are uncorrelated with each other, the pdf of the speckle, which is the sum of
If the
The
Hence, the pdf of the speckle in intensity can be approximated, by extension of Equation (12), for whatever value of
which is rigorously equivalent to Equation (12) when
All speckle models for multilook images (
Let us consider that the
For the intensity image resulting of the averaging of the
The exact pdf of the average intensity
Since the radar backscattering coefficient
Arsenault and April [15] [17] [19] demonstrated that, after this transformation, the pdf of the speckle for a multilook radar image, Equation (14), becomes a Fisher-Tippett distribution:
with
The first order statistical moments of this distribution are as follows [15]:
1) Mean <
Equation (18) shows that the logarithmic transformation causes a signal distortion, increasing with decreasing number
2) Variance
Lopès [20] has shown that for N independent
Texture concerns the spatial distribution of grey levels. It is important in the analysis of SAR images for a wide range of applications (
As seen above, within a homogeneous area image of a detected radar image, one can consider the observed speckle
In most remote sensing applications, it is reasonable to consider the imaged scene as an arrangement of discrete objects superimposed to a background with mean reflectivity E[
If
E[
E[
The spatial structure of actual imaged scenes, called "texture", induces a mesurable spatial structure (after the properties of
To characterise a given class
Considering the scene texture model of [25] in Equation (21), it is possible [27] [28] to model the radar image intensity at pixel of coordinates (
This relationship is valid as long as the spatial variations of
The variance of the intensity
Since the fluctuations of
and:
Therefore, E[
Introducing the coefficients of variation (
which characterises scene texture in terms of heterogeneity, with
For a better description of the scene, one must use the pdfs of its diverse classes (distribution of the random variable
where
To produce a SAR image, speckle samples at the output of the SAR processor are correlated through the SAR impulse response to obtain a sampling rate (pixel size) of about half the spatial resolution of the radar sensor, thus avoiding aliasing effects. Using the multiplicative noise model and Equations (24) and (25) [27] [28], the correlation coefficient
can be related to the scene correlation coefficient
Therefore, if
Using the simplified multiplicative noise model for the speckle
In practice,
Below the
The coefficient of variation
homogeneous areas with neither texture, nor structures.
areas with low or moderate heterogeneity where the presence of structures is improbable.
strongly heterogeneous areas where one must search for the possible presence of structures (contour, line, strong scatterers).
This section is dedicated to a theoretical analysis of the most common and more efficient speckle filtering techniques developed for Synthetic Aperture Radar (SAR) images, and of their corresponding estimation techniques. These filters use variants of the statistical speckle model exposed in the preceding Section 1. They also use diverse statistical estimators to restore the radar reflectivity (in the sense of the CEOS’s (Committee for Earth Observation Satellites) "radar brightness"). These statistical estimators are, either Minimum Mean Square Error estimators (
The SAR image model considered by [30] is as follows:
where
To estimate the radar reflectivity
The MMSE least-squares solution is valid for homogeneous areas for which
where
The impulse response of the filter is calculated adopting an autoregressive process model for
These hypotheses enable to define an optimal MMSE filter (Wiener filter) with impulse response
with:
where
The final Frost
The behaviour of the filter depends on the values of the locally observed coefficient of variation,
Extremely homogeneous areas, for which
Very strong scatterers (extreme heterogeneity), where
Between these two situations, in textured natural areas, when
Considering the requirements for an ideal speckle filter, the filter should:
Restore the mean intensity value over homogeneous areas, where
Preserve the observed intensity when the value of
Since it is impossible to fulfil simultaneously these two conditions (
The radar image
Kuan
where
The local statistics of the scene
Since the variance of the additive noise
It is noteworthy that, since the image model does not assume independence of the noise
Lee uses the unit-mean uncorrelated multiplicative speckle modele (
This approximation enables to transform Equation (33) into a weighted sum of the signal and of a noise independant on the signal. The linear MMSE estimator, and Equations (26) and (27) that are consequences of the multiplicative speckle model, enable to establish the Lee filter [33], historically the first speckle filter designed to be adaptive to local radar image statistics:
It is noteworthy that, assuming the independence of noise and signal in the model used by the Kuan
The linear MMSE speckle filters of Lee and of Kuan
with:
and
Both methods perform a linearly weighted average of the local mean of the intensity and of the observed pixel intensity. In both cases, weights depend on the ratio of the coefficients of variation of the observed intensity and of the noise [7] [40]. As for the Frost
Homogeneous areas where
Very stong scatterers (extreme heterogeneity) where
Between these two extreme situations, the weight of the observed value of the pixel intensity increases with the heterogeneity of its neighbourhood.
Nevertheless, note that the weight on the estimated mean intensity remains significant for high values of the coefficient of variation. Thus, responses of small impulse targets are cut-off by these filters and image structures suffer some amount of smoothing. To correct this drawback, Lopès
Inversely, when
The linear MMSE filters differ from the Wiener filters by the fact that the A Priori mean and variance, <
It is remarkable that the often used MMSE estimation ([33] [43] [44] [45] and [41] [32], among others) is nothing else than the mean of the conditional A Posteriori pdf P(
As an effect, this linearization restrains the validity of the linear MMSE estimator to situations where the noise level is not too high (multilook images with high values of
The Bayesian MAP approach [47] [48] consists in characterising the imaged scene and the speckle "noise" by their statistical description, using their associated pdfs.
In the Bayesian perspective, the theory of probabilities is extended to the logics of probabilistic inference. Probabilities are seen as a relationship between a formal hypothesis and a possible conclusion. This relationship corresponds to a certain degree of rational credibility and is limited only by the extreme relationships of certitude and impossibility [49]. The classic deductive logics considering only these extreme relationships (
As a general consequence, the theory of probabilities cannot base on the sole concepts of classic logics (frequentist as in the MMSE or in Wiener’s approaches). In particular, the relationship of probability cannot be defined in terms of certitude, since certitude is viewed as a particular case of probability. The frequentist definition of probabilities based on relations of certitude related to the knowledge of a number of parameters (<
In this context, probabilities are used to describe stochastically an incomplete information on a global phenomenon (here, the radar reflectivity and the superimposed speckle), rather than to describe only the noise randomness that corrupts its comprehension. Probability relationships are viewed as being conditional to the context. This way, the pdf of the speckle
The least error-cost inference mechanism leading from the observed intensity
P(
In theory, the MAP method enables to avoid direct estimation of the mean of the conditional A Posteriori pdf, which is necessary to the MMSE estimation. This feature is of great interest in the resolution of non-linear problems where the evaluation of the conditional mean is, either difficult, or impossible [51].
The Maximum A Posteriori (MAP) estimation of
P(
P(
there is no particular justification to prefer the MMSE estimation by minimisation of the mean quadratic error implicitly assuming a symmetrical P(
It is of interest to notice that the "Maximum Likelihood" estimation is nothing more than a particular case of Maximum A Posteriori, in the situation where the conditional A Posteriori and A Priori pdf’s are equal: P(
Once the first-order statistical speckle model P(
The NMNV scene model [32] enables to solve locally, in an analytic manner, the estimation of
Since the logarithm function is a monotonically increasing function, Bayes’s formula ([47] [48]; Equation (46)) can be rewritten as:
which gives the local MAP estimation of
with:
P(
Then, the general equation of the MAP speckle filter becomes, locally:
The first term of Equation (50), the Maximum Likelihood term, accounts for the effects of the whole imaging system on the radar image and describes the detected radar intensity once the speckle statistical model is known. The second term, the Maximum A Priori term, represents the prior statistical knowledge with regard to the imaged scene.
In the Bayesian approach, probabilities are used to describe incomplete information rather than randomness. As Equation (50) shows, in the Bayesian inference process, induction is influenced by the prior expectations allowed by the prior knowledge of P(
The pdf of the speckle in intensity for a
The Maximum A Priori term must be calculated according to the scene model chosen as A Priori knowledge, hypothesis, or belief.
The hypothesis of Gaussian-distributed scene has been adopted as a natural hypothesis by a large number of authors who had worked, either on images from optical sensors, or on images from passive/active microwave sensors. These authors are comforted in this hypothesis by both the force of habit and by the mathematical ease in manipulating a Gaussian distribution.
Kuan
Therefore, one must preferably take into account a positive pdf as a realistic scene model. For reasons that are both experimental and theoretical, in the case of natural extended targets as it is most often the case dealt with in remote sensing, a Gamma distributed scene model is better appropriate.
Natural textures observed, either by coherent radar imagery, or by incoherent optical imagery are due to a common contribution corresponding to the variability in spatial distribution of the objects within the scene. Even if the interaction mechanisms between the electromagnetic wave and the observed medium are very different in either case, the natural arrangement of the scene makes the second-order statistics very similar in either kind of imagery [55] [56]. At the scale of a large number of resolution cells, the pdfs of the cross-section variables corresponding to either mechanism belong to the same family of distributions, at least for the high radar frequencies in bands Ku, X, and C for which wave penetration into natural media is limited. This point is more arguable for radar bands L and P.
In a wide range of radar backscattering situations, the Gamma distribution is experimentally the one that best fits, not only the distribution of the radar backscattering coefficient [21] [57], but also the distribution of radiometries observed in incoherent optical images [55]. This scene model has been successfully used also for radar images of the sea [58] [59] [60].
The local pdf of a scene statistically described by a Gamma distribution, has the form:
with
Note that assuming a Gamma-distributed
Nevertheless, in an illustration of the so-called "Cromwell’s rule" [62], even hard A Priori conviction that the scene presents a Gamma-distributed texture must not be insensitive to counter-evidence. Therefore, the complete MAP filter is a set of three filters adapted to diverse situations locally encountered in a radar image: the application of this or that filter is decided depending on the degree of heterogeneity of the image part under processing, that is on the locally estimated value of the coefficient of variation
Assuming that the pdf of the scene is a Gamma distribution, the Maximum A Priori term is locally equal to:
This second-degree equation admits only one real positive solution
The integration of a heterogeneity/texture detector based on the coefficient de variation and of specific detectors (ratio-of-amplitudes – RoA; [66] [7]) for contours, linear structures and strong scatterers in the filtering process is described by the general algorithm presented in [63] [64] [10]. The integration of texture and structure detectors using second-order statistics (autocorrelation functions) of both the speckle and the radar reflectivity of the scene [67] is presented in Section 4. In all cases, mage areas identified as textured are filtered using Equation (54).
In the particular case of a perfectly homogeneous (textureless) scene, with
This distribution is the limit of Gamma distributions when
This case is taken into account when the local statistics calculated in the neighbourhood of the processed pixel show a nearly perfect homogeneity of the scene.
The other extreme case regards strong scatterers, when speckle is no longer fully developed. The considerations that led to consider the Gamma pdf as an A Priori model for a textured natural scene are no longer valid: we no longer have any A Priori information about the scene. In this situation, the information content of every grey level of the image being A Priori the same, the pdf of the imaged scene can be represented by an uniform distribution:
with undetermined extreme values
If the resolution cell contains only one isolated strong scatterer, the response (value
SAR images of dense tropical forest, urban areas, or very strong and rapidly varying topography often show very strong or mixed textures. This is also the case of high-and very-high spatial resolution SAR images. In these situations, it may be hazardous to make an assumption about the probability density function of the radar reflectivity.
Indeed, the MAP technique does not account for any uncertainty in the value of the parameters of the A Priori pdf chosen as a Gamma distribution once it has been locally estimated on a given image area. Hence, in the presence of mixed (forests with underlying structures, for example) or rapidly varying (strongly textured area located on strong slopes, or very-high spatial resolution radar images, for example) texture, the MAP estimator will underestimate the variance of the predictive distribution. Indeed, this predictive distribution can hardly take into account the fact that it results of a compound of a mix of different distributions.
In this context, the A Priori knowledge with regard to the observed scene can hardly be an analytical first order statistical model, chosen on the base of prior scene knowledge. However, to retrieve local statistical scene knowledge directly from SAR image data, Datcu & Walessa [68] [69] proposed to introduce the local entropy of the radar reflectivity,
Because the radar reflectivities
For a single detected SAR image, the conditional pdf of the speckle can be, as long as speckle is fully developed, modelled as a Gamma distribution:
Incorporating these scene and speckle models, the Gamma/Distribution-Entropy MAP (Gamma-DE MAP) speckle filter for single-channel detected SAR images is the solution of the following equation [71]:
The radar reflectivites
Note that the local DE MAP estimation of
The DE-MAP filters adapt to a much larger range of textures than the other MAP filters ([10] [11] [72] [73]) developed under the assumption of K-distributed SAR intensity (
Compared to those of the other MAP filters, performances in terms of speckle reduction are identical. However, texture restoration and structures or point targets preservation, identical for moderate textures, are superior in strongly textured areas. These filters have proven a remarkable efficiency in operational remote sensing (
The local MAP estimate is the mode of the local A Posteriori pdf of the radar reflectivity
In the presence of texture, the estimator takes into account the non-linear effects in getting from
Nevertheless, in the presence of structures, the NMNV model may be used abusively on an inappropriate neighbourhood of the pixel under processing and therefore the local statistics <
The assumptions usually made by adaptive speckle filters ([76] [30] [32] [10]) with regard to the first order statistics of the speckle are:
the multiplicative speckle model;
the pdf of the speckle which reflects only incompletely the spatial correlation of the speckle through the ENL.
The assumptions regarding the imaged scene are:
Local wide-sense stationarity and ergodicity;
A formal Non-stationary Mean Non-stationary Variance (NMNV) model accounting for the spatial variation of the first order statistical properties [32];
For the Maximum A Posteriori (MAP) filter, the general form of the pdf of the scene radar cross-section [10].
The main consequences of these assumptions are that:
The formal NMNV model justifies local treatment (processing window) using the local statistics;
Assuming spatially uncorrelated speckle leads to the design of scalar (single-point) filters;
Local adaptivity to tonal properties (mean intensity <
The major drawbacks of these assumptions are that:
The coefficients of variation are statistically [10] sensitive to texture and speckle strength [40], but do not provide direct information on spatial correlation properties and texture directionality. Locally estimated, they can also be biased if speckle samples are not independent (
Compliance with the ergodicity and stationarity hypothesis requires a preliminary identification of the structural elements of the scene to fully exploit the NMNV model and to correctly estimate the local mean reflectivity around the pixel under processing;
In this Section, local second order properties, describing spatial relationships between pixels are introduced into single-point speckle adaptive filtering processes, in order to account for the effects of speckle and scene spatial correlations. To this end, texture measures originating from the local autocorrelation functions (ACF) are used to refine the evaluation of the non-stationary first order local statistics as well as to detect the structural elements of the scene [75].
In practice, the usual single-point filters do preserve texture, only due to the spatial variation of the local first order statistics, using the NMNV formal model [32]. Fairly good preservation of textural properties and structural elements (edges, lines, strong scatterers) can be achieved by associating constant false alarm rate structure detectors such as the directional Ratio-Of-Amplitudes (RoA) detectors; ([7] [66] [10] [63] [64]) to the speckle filtering process. The combination of detection and speckle filtering allows to preserve scene structures, and to enhance scene texture estimation on a shape adaptive neighborhood of the pixel under consideration. When the conditions for which they have been developed (especially spatially uncorrelated or low-correlated speckle) are fulfilled, the best single-point adaptive speckle filters and their structure retaining associated processes based on RoA detectors retain enough scene texture to allow its use as an additional discriminator ([23] [24] [63] [64]).
However, the performances of the usual single-point filters, even when refined with associated RoA structure detectors, degrade when the actual spatial correlation of speckle samples becomes significant [77],
As an example of the effects of speckle spatial correlation, the case of structure detection using the RoA detectors [10] implemented in the Gamma-Gamma MAP filter is illustrated in [67]. Detection is successfully performed when the correlation of speckle is low between pixels. However, the performance of RoA detectors substantially degrades when the spatial correlation of speckle samples becomes significant. It has been shown theoretically in [67] that spatial speckle correlation results in an increasing detection of non-existing structures,
Therefore, second order statistical properties must also be considered, including both scene texture and resolution/sampling related properties, to achieve a more complete restoration of the radar reflectivity.
A possible solution is the implementation of vector (multiple-points) filters ([32] [79]) where the spatial covariance matrices of the speckle and of the scene are taken into account. The development of a filtering method using second order statistics,
To avoid the mathematical complexity and the heavy computational burden of multiple-point filters, an alternative solution is to introduce an appropriate description of both speckle correlation properties and spatial relations between resolution cells into a single-point filter.
Second order statistics have explicitly been used in the past, following the scheme proposed by Woods & Biemond [80] implemented later by Quelle & Boucher [81] in the adaptive Frost speckle (single-point) filter [30]. The filter, which belongs to the family of Wiener filters, is established using Yaglom\'s method [38], where a frequency characteristic is determined, instead of an impulse transfer function, which actually exists only within wide-sense stationary areas. This frequency characteristic is determined in the speckled image at a distance of one pixel only in range and azimuth, thus mainly limiting the description of correlation properties to those of the speckle.
Considering the NMNV model [32], a much better solution is to introduce locally estimated second order statistics to refine the computation of the local NMNV first order statistics. In this way, speckle correlation properties can be taken into account for filtering. In addition, the local mean radar reflectivity E[
Spatial relations between pixels are well described by the intensity ACF, defined by a set of correlation coefficients
The local estimates for the normalised intensity ACF,
or, equivalently, directly computed within the domain of interest
These estimates have the attractive property that their mean square error is generally smaller than that of other estimators; considerations on the estimation accuracy can be found in Rignot & Kwok [82].
The contribution of scene texture must be separated from that of the speckle for all displacements Δ
where
where
The computation of a local non-stationary estimate of E(
for all
This means that, along a direction
The implementation of this operator, which estimates the local non-stationary E[
in homogeneous areas, Equation (67) acts as a smoothing operator:
for a strong scatterer, it acts as a Laplacian:
if a perfect step edge between two homogeneous areas is present in the neighbourhood (correlations=1,0,-1), it acts as a gradient and automatically stops weighting further pixels. This indicates structure detection capability;
in all other cases,
This concept of "texture fields", based on the analysis of second order statistical properties, generalises image processing concepts such as filtering and detection, which are usually considered as distinct. It provides the possibility to perform simultaneously structure detection and image pre-filtering. Structural elements such as edges are detected (value of the estimate of the correlation coefficient becoming less than 1/e) when the distance from the pixel under processing (central pixel of the processing window) to the edge is reached, going in the direction of the edge. Therefore, the position of the edge is known as soon as the edge enters the “field of view” of the processing window.
The local mean radar reflectivity estimation takes now into account the textural properties of the imaged scene (spatial variability of
The whole process acts as an adaptive focusing process in addition to the filtering process, since emphasis is put on the restoration and the enhancement of the small-spatial-scale fluctuations of the radar reflectivity. Full profit is taken of the useful resolution offered by the compound sensor and processing systems. Thus, speckle related features and thin scene elements (scene short-scale texture and thin structures) are automatically differentiated. The latter are restored with enhanced spatial accuracy, according to the local statistical context.
There is no geometrisation of the scene structural elements, as when using templates-based detectors used in structure retaining speckle filters ([76] [63] [64] [10]). The neighbourhood on which the NMNV local statistics are estimated is delimited by the estimates of
Since the launch of ERS-1 satellite in 1991 temporal series of calibrated SAR images have been made available. This has stimulated the development of multichannel ("vector") filters especially dedicated to filter the speckle in a series of images, taking into account the correlation of the SAR signal between image acquisitions, thus opening the way to furher developments of change detection techniques specific to SAR series of images.
Interest in multichannel adaptive speckle filtering arises from their ability to combine both multi-image diversity through the exploitation of correlation between speckle and scene between images, and spatial diversity through the locally adaptive averaging of pixel values in the spatial domain. The objectives of this combination are the simultaneously achievement of both, a better restoration of existing texture, and a stronger speckle smoothing in textureless areas.
Although numerous work with regard to multi-channel speckle filtering in SAR images had already being done in a remote past, the introduction of A Priori knowledge or A Priori guess which implies the use of Bayesian methods in the processing of multi-SAR’s images, multi-date SAR images or SAR and optical images began only in 1992. Bayesian MAP vector speckle filters developed for multi-channel SAR images incorporate statistical descriptions of the scene and of the speckle in multi-channel SAR images. These models are able to account for the scene and system effects, which result in a certain amount of correlation between the different channels.
To account for the effects due to the spatial correlation of both the speckle and the scene in SAR images, estimators originating from the local ACF’s (
In the case of multi-channel detected SAR images, let define the vector quantities of interest:
where P(
For each individual detected SAR image
P(
For multi-channel detected SAR images, MAP filtering is a vector filtering method. For every channel
The MAP speckle filtering process acts as a data fusion process, since the information content of the whole image series is exploited to restore the radar reflectivity in each individual image. Among other advantages, this allows a better detection and restoration of thin scene details.
To describe the first order statistical properties of a natural scene viewed at the spatial resolution and at the wavelength frequency of a radar remote sensing sensor, it has been shown that a Gamma pdf would be a suitable representation (
However, to describe the first order statistical properties of a natural scene as viewed by diverse radar sensors (different physical scene features), or at different dates (scene evolution over time), there is no analytic multivariate Gamma pdf available under closed-form. Therefore, for the sake of mathematical tractability, a multivariate Gaussian pdf is used as analytic "ersatz" of a multi-channel scene statistical model:
This statistical scene model is convenient to preserve the mathematical tractability of the problem. In addition, the Gaussian model, commonly used to describe the statistical properties of natural scenes in processing of optical imagery, is appropriate as a first-order statistical description of moderate, not too heterogeneous, textures.
However, when scene texture becomes very heterogeneous, this multivariate Gaussian scene model is not any longer reliable, and in the absence of specific prior knowledge about the distribution of scene texture, its model has to be directly estimated from the SAR image.
Let consider the case of a series of
Under this assumption, since each one of the SARs senses slightly different physical properties of the same scene, the joint conditional pdf of the fully developed speckle, P(
where the
The first MAP filtering algorithm for multi-channel detected SAR images results in a set of
where (1
The introduction of coupling between the scene statistical representations leads to a data fusion process taking profit of the correlation between texture as it is observed in all the images in the series. Indeed, replacing speckle noise model by optical noise (or film grain noise, or any other appropriate noise model) in some of the
To filter series of images originating, either from the same SAR system operating in repeat-pass mode, or from different SAR systems with relatively close properties, a second speckle filtering algorithm has been developed. The different SARs may be close in terms of frequency, angle of incidence, spatial resolution and sampling, geometry of images, with difference in polarisation configuration only, or small differences in incidence angle, for example. In such cases, speckle correlation between individual SAR images must be taken into account to deal optimally with system effects on the SAR images series.
Taking into consideration speckle correlation between image channels, the joint conditional pdf of the fully developed speckle P(
To solve this problem, Lee’s assumption [33] is adopted: for multilook SAR images (more than 3 looks), the joint conditional pdf of the speckle can be reasonably approximated by a Gaussian distribution. Therefore, for convenience, P(
where
It is noteworthy that
At this point, the second MAP filtering algorithm for multi-channel detected multilook SAR images results in a set of
where
Including a coupled speckle model (Equation (71)) and a coupled scene model (Equation (74)), this speckle filter takes profit of both speckle and scene texture correlations, thus restoring the radar reflectivity through a complete data fusion process.
In the presence of very strong or mixed textures, eventually combined with the presence of strong topographic relief, it may be hazardous to make an A Priori assumption about the pdf of the radar reflectivity. This situation is often the case in SAR images of dense tropical forest or of urban environments located in slanted terrain with rapidly varying slopes and counter-slopes.
This is also often the case in high-and very-high spatial resolution SAR images, where strong texture is omnipresent. In addition, at such very fine scale, the textural properties of the scene vary strongly within the timeframe elapsed between successive image acquisitions by the same SAR, and exhibit strong differences when imaged by different SARs or using different SAR configurations in terms of wavelength, polarisation, or angle of incidence, for example.
As mentioned precedently (
The entropy of the scene texture [68] [69] [70] is introduced (on a sample of
Because the radar reflectivities
Under this assumption, the Gaussian/Distribution-Entropy MAP (Gaussian-DE MAP) filter for multi-channel multilook detected SAR images (
where (l
To estimate P(
Note that this filter does not need to introduce implicitly scene texture correlation. Indeed, the restoration of the radar reflectivity in each image channel bases only on the A Priori knowledge retrieved from the speckled image channel. Nevertheless, the filter takes profit of speckle correlation between image channels. An application of this filter is illustrated in Figure 1.
TerraSAR-X 4-looks SAR images (HH and VV polarisations) acquired on August 10, 2007, over Oberpfaffenhofen, Germany (Credits: Astrium and InfoTerra; filtered images: Privateers NV, using ®SARScape).
Note that if speckle is not correlated between the
A number of advantages and improvements these filters offer is listed in this section. Most of these advantages arise from the use of the covariance matrices of the speckle and of the imaged scene (
In a series of SAR (but not only...) images, the resolution cells never overlap perfectly between the different individual images. The covariance matrix of the speckle
In addition, the simultaneous attempt to detect images structures and targets in all radar image channels improves the probability to detect such structures. As shown in Figure 1, such an improvement is already very substantial using only two SAR images.
In series of radar images acquired over time, the covariance matrix of the scene
In a series of SAR images acquired by (not too...) different SAR’s, the covariance matrix of the scene enables the detection of more aspects of the scene, as it is viewed by more SAR sensors.
Let consider the mathematical form of the set of Equations (73), (75) and (79). Indeed, their formulation is that of a control system, since both equations can be rewritten under the form of Riccati’s [86] continuous time algebraic matricial equations:
Equation (10) represents the optimal state controlled reconstruction at constant gain of linear invariant processes (
In addition, it is highly remarkable that Riccati’s theorem [86] stipulates the existence of a unique positive definite solution for Equation (10). Therefore, this property holds also for the MAP Equations (73), (75) and (79).
The extension of this noise filtering technique to the case of SAR and optical images set is straightforward, as mentioned in § 5.3. It is also noticeable that, extending this method to the case of complex SAR images, this technique presents a very interesting potential for super-resolution. Such methodologies can be found in the literature (
A major interest of control systems is that they offer wide possibilities for the choice and design of additional commands (statistical and physical models) for further data exploitation. In this view, speckle filtering should be regarded as a first step of integrated application oriented control systems.
SLC images are complex valued images. Hence, every image pixel is assigned a complex value, its complex amplitude
Whereas the intensity
Therefore, the quantity of interest to restore in SLC images through speckle filtering is the radar reflectivity
In SLC radar data the spatial correlation of the speckle can be dealt with by taking into account the spatial covariance matrix of the speckle in the filtering process. Indeed, the joint pdf of a local sample
where
In high and very-high spatial resolution SLC SAR images, which often exhibit strong textural properties, it becomes difficult to consider any theoretical statistical model as a reliable A Priori knowledge about scene texture. In such a situation, it seems reasonable to retrieve statistical scene knowledge directly from the SAR image data [68] [69]Introducing the local entropy of the radar reflectivity as A Priori scene knowledge [70] (
where
An example of application of this filter is shown in Figure 2.
RADARSAT-2 1-look SAR image (HH polarisation) acquired on April 30, 2009, over Capetown, South Africa (Credits: Canadian Space Agency and MDA; filtered images: Privateers NV, using ®SARScape).
Interest in adaptive speckle filtering of SLC image series arises from their ability to combine both temporal or spectral diversity, and spatial diversity. The objective is to obtain a multilook radar reflectivity image, where additional speckle reduction is obtained in the spatial domain by averaging pixel values through locally adaptive filtering.
The final objective is to obtain very strong speckle smoothing in textureless areas and to reach an ENL high enough (
A SAR sensor can produce series of SLC SAR images in three different situations:
In the case of a non-interferometric 1-look complex SAR data set, the individual SAR SLC images acquired independently of each other show low or inexistent speckle correlation between images.
However, in the case of interferometric 1-look complex SAR data, speckle correlation is close to 1 (corresponding to perfect correlation) or very high, since speckle correlation between SAR data acquisitions is exactly the fundamental requirement for interferometry.
As a consequence of these three possible configurations of a series of SLC images, a speckle filter dealing with the whole series must be able to take into account the complex correlation of speckle between the different images. The local complex covariance matrix
Considering the whole set of SLC images, the measurement vector for each pixel is
where
In the case of separate complex looks corresponding to the same SAR data acquisition, or of series of SLC images acquired over time by the same SAR over a time-invariant scene, it is quite reasonable to assume that the textural properties of the scene are similar in all the SLC’s.
Therefore, scene texture may be locally modelled for all SLCs in the series, as well as in the detected multilook image formed by incoherent pixel-per-pixel averaging, by the same Gamma pdf with parameters E[
In this situation, the Complex-Gaussian/Gamma MAP (CGs-Gamma MAP) filter for separate complex looks (
Note that when the SLC images or the separate looks are independent,
Finally, it is noteworthy that the ENL
This relationship may prove useful if SLC images are incoherently summed to produce a
High and very-high spatial resolution SLC SAR images often exhibit strong textural properties. As exposed above, in such a situation, statistical scene knowledge is estimated from the radar data [68] [69]Introducing the local entropy of the radar reflectivity as A Priori scene knowledge [70] (
where
An application of this filter to a series of 18 (3x6) SLC SAR images is shown in Figure 3.
Series of 18 (3x6) COSMO-SkyMed 1-look spotlight SAR images (HH and VV polarisations) acquired between February 2 and September 30, 2009, over Perito Moreno in Patagonia, Argentina (Credits: original images: Italian Space Agency; filtered images: Privateers NV, using ®SARScape).
A polarimetric radar system produces, for each pixel location, a scattering measurement matrix, which is the scattering matrix
The first polarimetric speckle filter ever developed [92] resulted in an optimal summation of the intensities
where
However, Equation (87) clearly shows that the ENL achieved in the span image will barely reach 3, which remains largely insufficient to meet the noise reduction requirements of remote sensing applications. Besides, the physical interpretation of
Lee
Nevertheless, the ultimate aim of polarimetric speckle filtering is to restore the full polarimetric scattering matrix
Under the assumption of reciprocity,
This representation puts emphasis on the radar reflectivities
If the polarimetric measurement is made by a monostatic radar system, the covariance matrix ΣS of the actual polarimetric radar measurement is the speckle-corrupted version of CS.
It is noteworthy that it is possible to produce multilook polarimetric data (
For convenience, the polarimetric covariance matrix is often expressed, without changing its overall information content, under the form of a real valued vector, called the "polarimetric feature vector", or "polarimetric vector"
Two quantities of great interest in polarimetric SAR applications can be obtained from the polarimetric vector:
1) The Phase Differences Δ
2) The (complex) Degrees of Coherences
However, in the actual polarimetric radar measurement, the observations Δ
In the case of polarimetric radar data, ΣS is the actually observed polarimetric covariance matrix, and CS is the unspeckled polarimetric covariance matrix,
where
Using physical backscattering models, assuming (as a rough approximation) that texture is identical in all polarizations, we get the following approximation [72]:
where
The characterisation of scene heterogeneity by only one textural random variable
Introducing the first-order statistical models for fully-polarimetric speckle (Equation (93)) and the polarimetric texture parameter
where Tr(.) denotes the trace of a matrix.
Finally, the restored (speckle filtered) version of the covariance matrix C
In high and very-high spatial resolution polarimetric SAR data, strong and/or mixed textural properties justify to estimate statistical scene knowledge from the data themselves, rather than assuming a A Priori theoretical model. Assuming that textural properties are identical in all configurations of polarisation, the entropy constraint on scene texture ([68] [69] [70]) becomes:
In this case, the complex Wishart/Distribution-Entropy MAP (CW-DE MAP) filter for polarimetric multilook SAR data is expressed as [71]:
E[CS] is obtained using the maximum likelihood estimator (Equation (97)) described in Lopès
ALOS-PALSAR 6-looks polarimetric SAR imagery acquired on June 30, 2006 over Bavaria, Germany (Credits: JAXA and MITI; filtered images: Privateers NV, using ®SARScape).
The texture parameters
Although these fully polarimetric MAP filters assume identical texture properties in the HH, HV, and VV channels, which has been shown both experimentally [96] and theoretically [97] inexact, they have nevertheless been shown to preserve polarimetric signatures [98]. Fully polarimetric speckle filtering is illustrated in Figure 4.
ACF: spatial AutoCorrelation Function (of a signal)
DE: Distribution-Entropy (statistical distribution)
ENL: Equivalent Number of Looks (of a radar image)
LMMSE: Linear Minimum Mean Square Error (estimation)
MAP: Maximum A Posteriori (statistical estimation)
MMSE: Minimum Mean Square Error (estimation)
NMNV: Non-stationary Mean Non-stationary Variance (model)
pdf: Probability Density Function (random variable distribution)
Pfa: Probability of False Alarm (detection)
PSF: Point Spread Function (of an imaging system)
RoA: Ratio Of Amplitudes (structure detector)
SAR: Synthetic Aperture Radar (imaging sensor)
SLC: Single-Look-Complex (radar image)
Hydroponic techniques have been developed to facilitate cultivation under diverse environments and to improve farming practices using soilless methods. In this novel world, hydroponic farming makes efficient use of fertilizers and water, increases productivity, and provides better crop quality; Table 1 shows the difference in productivity between soil and soilless culture for different crops. Also, due to the risks of soil and water contamination in metropolitan areas, this technique has a potential alternative to agricultural production in cities. Hydroponic systems irrespective of their scale reduce dependence on the soil as a substrate and instead derive nutrition directly from the hydroponic solution comprising of water and nutrients [2]. Because hydroponics provides better control of plant growth, it is possible to achieve high quality and productivity through careful management of—nutrient composition, dissolved O2 concentration, temperature, pH, and electrical conductivity (EC) of the nutrient solution. Nutrient supply in hydroponics can significantly influence the nutrition, taste, texture, color, and other characteristics of fruit and vegetable crops [3]. In hydroponics, essential nutrient elements are dissolved in appropriate concentrations and relative ratios to achieve the normal growth of plants [4]. It is well known that the productivity and quality of crops grown in hydroponic systems are markedly dependent on the extent of the plant nutrients acquisition from the growing medium [5]. Due to this, nutrient solution and its management are the cornerstone for a successful hydroponics system and are the most important determining factors of crop yield and quality.
Crop | Hydroponic system production (Kg/ha) | Open-field production (Kg/ha) |
---|---|---|
Rice | 13,456.56 | 841.03–1009.25 |
Maize | 8971.0 | 1682.07 |
Peas | 15,699.32 | 2242.76 |
Tomato | 403,335.81 | 11,203.75–22,407.47 |
Potato | 156,852.29 | 17,925.98 |
Cabbage | 20,184.84 | 14,577.94 |
Cucumber | 31,398.64 | 7849.66 |
Lettuce | 23,548.98 | 10.092.42 |
Yield comparisons for different crops between hydroponic and open field cultivation.
Source: Singh and Singh [1].
Plant nutrients used in hydroponics are dissolved in water and are mostly in inorganic and ionic forms. All the essential elements for plant growth are supplied using different chemical combinations and establishing a nutrient solution that provides a favorable ratio of ions for plant growth and development is considered an important step in cultivating crops in hydroponic systems [6]. Plant uptake of nutrients can only proceed when they are present in an available form for absorption, and in most situations, nutrients are absorbed in an ionic form. Ions are electrically charged forms of each nutrient, some are cations (positively charged) and others are anions (negatively charged). For example, nitrogen is absorbed as ammonium (NH4+, a cation) or nitrate (NO3−, an anion); Table 2 shows the available form of each nutrient and different nutrient solution formulas which have been established by many scientists. There are various standard nutrient solutions, such as the Hoagland and Snyder [13], Hoagland and Arnon [11], Steiner [14] Bollard [15], and others. These standard solutions are good as a general guideline but are not adapted to specific growing conditions. The function of a hydroponics nutrient solution is to supply the plant roots with water, oxygen, and essential mineral elements in soluble form. A nutrient solution usually contains inorganic ions from soluble salts of essential elements required by the plant. However, some organic compounds such as iron chelates may be present [16]. A total of 17 elements are considered essential for most plants, these are carbon, hydrogen, oxygen, nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, iron, copper, zinc, manganese, molybdenum, boron, chlorine, and nickel [17]. An essential element has a clear physiological role, and its absence prevents the complete plant life cycle [18]. Among the minerals, N, P, and K are the most decisive elements in plants [6]. Some other elements such as sodium, silicon, vanadium, can stimulate growth, or can compensate for the toxic effects of other elements, or may replace essential nutrients in a less specific role. Tahereh et al. [19] reported that the plants grown in the absence of silica would be weak and show abnormal growth, and proper application of this nutrient can increase consistency and disease resistance, reduce the outbreak of nutrient deficiencies, improve product quality and increase crop yield. In hydroponics, all the nutrients are in a balanced ratio which is directly supplied to the plants, and composition must reflect the uptake ratio of individual elements by the crop, as the demand between species differs, and must be specific for each crop [20]. It is very important to keep ionic balance in the nutrient solution since plant growth and productivity can be negatively affected by the improper relationship between the essential nutrients, that is, the ratio of anions: NO3−, H2PO4− and SO42−, and the cations K+, Ca2+, Mg2+ [21], and a change in the concentration of one ion must be accompanied by either a corresponding change for an ion of the opposite charge, a complementary change for other ions of the same charge, or both [12]. However, for most common crop plants, critical levels for most nutrients have been determined [22].
Element | Form taken up by plants | Hoagland & Arnon | Hewitt | Cooper | Steiner |
---|---|---|---|---|---|
mg L−1 | |||||
Nitrogen | NH4+, NO3− | 210 | 168 | 200–236 | 168 |
Phosphorus | HPO4−2, H2PO4− | 31 | 41 | 60 | 31 |
Potassium | K+ | 234 | 156 | 300 | 273 |
Calcium | Ca2+ | 160 | 160 | 170–185 | 180 |
Magnesium | Mg2+ | 34 | 36 | 50 | 48 |
sulfur | SO42− | 64 | 48 | 68 | 336 |
Iron | Fe2+, Fe3+ | 2.5 | 2.8 | 12 | 2–4 |
Copper | Cu2+ | 0.02 | 0.064 | 0.1 | 0.02 |
Zinc | Zn2+ | 0.05 | 0.065 | 0.1 | 0.11 |
Manganese | Mn2+, Mn4+ | 0.5 | 0.54 | 2 | 0.62 |
Boron | H3BO3, BO3−, B4O72− | 0.5 | 0.54 | 0.3 | 0.14 |
Molybdenum | MoO42− | 0.01 | 0.04 | 0.2 | Not considered |
Nutrients in the nutrient solution have great interactions that may gain either positive or negative effects on crop production, depending on crop growth stages, amounts, combinations, and balance [23]. Inadequate or excessive concentrations of minerals or an imbalanced ion composition in the nutrient solution may inhibit plant development, resulting in toxicity or nutrient-induced deficiencies [24]. In crop plants, the nutrient interactions are generally measured in terms of growth response and change in concentration of nutrients. Nutrient interactions may be positive or negative and also possible to have no interactions. Interaction between nutrients occurs when the supply of one nutrient affects the absorption and utilization of other nutrients. This type of interaction is most common when one nutrient is in excess concentration in the growth medium. Upon the addition of two nutrients, an increase in crop yield that is more than adding only one, the interaction is positive (synergistic). Similarly, if adding the two nutrients together produced less yield as compared to individual ones, the interactions are negative (antagonistic). When there is no change, there is no interaction. However, most interactions are complex and better understanding of nutrient interactions may be useful in understanding the importance of a balanced supply of nutrients and consequently improvement in plant growth or yields [25]. According to Marschner [26], at the level of the nutrient acquisition mechanisms, competitive or antagonistic phenomena among elements can occur, for example, the interaction between NH4+ and K+, and this could be crucial for NH4+ fed plants when exposed to a suboptimal/unbalanced availability of K+ because the competition could induce/exacerbate K+ deficiency [27], and it is more relevant when the additional application of NH4+ is of pivotal role to achieve specific qualitative objectives of the edible fruits [28]. The interactions between K+/Na+ and Cl−/NO3− could represent a limiting factor for soilless cultivation of crop plants, especially in a semiarid environment characterized by saline water. NaCl interferes with the uptake processes of both K+ and NO3−, since K+ is sensitive to Na+ in the external environment, while the uptake of NO3− is inhibited by Cl− [29]. This phenomenon could be even more pronounced in hydroponic solutions particularly when used in a closed system, where monitoring the ratio between Ca2+, Mg2+, and K+ in the solutions is very important to avoid K+/Ca2+ induced Mg2+ deficiency. Calcium, magnesium, and potassium compete with each other and the addition of any one of them will reduce the uptake rate of the other two [26]. Unbalanced fertilization practice, with a high level of K+ and Ca2+, can induce Mg2+ deficiency in crop plants, Schimansky [30] suggested that the excessive availability of K+ and Ca2+ could inhibit Mg2+ uptake by roots. Similarly, very high rates of Mg2+ fertilizers will depress K+ absorption by plants, but this antagonism is not nearly as strong as the inverse relation of K+ on Mg2+ [31]. Also, the uptake of nitrogen, sulfur, and iron is not exclusively dependent on its availability in the hydroponic solution but also on the presence of other elements. The uptake of NO3− was hampered by the shortages of iron and sulfur, and the effect on the assimilation process seems to play a dominant role in determining the NO3− accumulation at the leaf level. In the case of nitrogen and sulfur, the lacking one represses the assimilation of the other and induces physiological changes aiming at re-balancing the contents in the plant [32]. One of the greatest issues concerning hydroponic productions is sulfur starvation due to a consistent accumulation of NO3− in plant leaves [33]. The anion which is taken up relatively slowly can also reduce the uptake speed of its counter-ion, as observed for SO42− on K+ uptake [26].
In hydroponic solutions, interactions among solutes cannot be neglected and therefore ion activity should be used in calculations instead of concentrations [34]. The high ionic concentrations can disrupt membrane integrity and function, as well as internal solute balance and nutrient absorption, resulting in nutritional deficiency symptoms similar to those observed when nutrient concentrations are below the required levels [24]. In addition, the root physiological process is not only affected by the availability levels of the nutrients, but also by the nutrient sources and/or by the interactions among the different nutrients [35]. The chemical forms of a nutrient are also very important, for example, plants can use a wide variety of nitrogen forms, ranging from the inorganic, namely NH4+ and NO3−, to the organic ones, like urea and amino acids [36]. Ammonium is an attractive nitrogen form for root uptake due to its permanent availability and the reduced state of the nitrogen; nevertheless, when both nitrogen forms are supplied to the nutrient solution, plant roots may absorb preferentially one of them, depending on the heredity of each specie [37]. Pure NH4+ nutrition caused the development of toxicity symptoms in many herbaceous plants, as well as inhibited NO3− uptake [38]. Therefore, a balanced nitrogen diet is clearly beneficial for several plant species as compared to that based exclusively on NO3− [39]. Tomato root growth was optimal when NO3− and NH4+ were supplied in a 3:1 ratio; on the contrary, when NH4+ concentration was too high, a strong inhibition in the root development was observed [40]. However, the form of nitrogen suitable for obtaining the maximum production for each species and its cultivation conditions has not yet been defined [37]. Also, the plant species and environmental conditions are two critical factors that affect the optimum NO3−/NH4+ ratio, which can affect not only root development and morphology but also the overall root biomass. According to [41], the chemical quality of nutrient solutions can affect plant yield and bioactive compounds.
Several physical-chemical phenomena can alter the nutrient availability for plants, the most important of which are—precipitation, co-precipitation, and complexation. Precipitation reactions may occur when cations and anions in an aqueous solution combine to form a precipitate. It is known that phosphate availability can be reduced at pH above 7 mostly due to precipitation with calcium and different calcium-phosphate minerals can potentially form above this pH [42], and precipitation of phosphates must be avoided in hydroponic solutions because it is not only depleting phosphorus from the nutrient solution, but it may also reduce the solubility of other nutrients, such as calcium, magnesium, iron, and manganese. Also, sulfur availability can be limited by precipitation with calcium, as calcium-sulfate minerals [43]. Co-precipitation also may strongly reduce the solubility of nutrients added at trace concentrations, such as copper, zinc, manganese, and nickel, when insoluble compounds, such as iron hydroxides, calcium carbonates, or calcium phosphates, are formed [44]. In hydroponic solutions, a complex chemical compound is formed when a metal nutrient is bound by one or more neutral molecules or anions, either of organic or inorganic nature. The resulting complex can be a neutral compound, a cation, or an anion, depending on whether positive or negative charges prevail. These reactions diminish the concentration of the free ions in the nutrient solution, changing elemental bioavailability. The addition, organic ligands, such as: ethylenediaminetetraacetic acid (EDTA), Diethylenetriamine Penta acetic Acid (DTPA), Ethylenediamine (O-Hydroxyphenyl acetic) Acid (EDDHA), and citrate, can increase the stability of certain elements in solution, especially iron, copper, and zinc [45].
The pH value of the nutrient solution greatly affects plants’ growth. This is because the nutrients added to the nutrient solution are available for the uptake by the plant are soluble in water only at particular pH levels, as shown in Figure 1. According to Mayavan et al. [47], the plants require a range of pH values to be maintained to ensure the availability of all the nutrients for uptake by the plants. Nutrient solution pH is typically managed between 5.5 and 6.5, and it seems to be a range where almost all hydroponically grown crops exhibit normal growth and nutrient uptake, and the optimum pH range for different crops grown hydroponically are shown in Table 3. However, species-specific pH responses of leafy greens grown in liquid culture hydroponic systems are largely unexplored [49]. However, the optimum pH for maximum growth differs not only between species, but also between cultivar, climatic conditions, and soil, substrate, or nutrient solution conditions [50]. Frick and Mitchell [51] indicated that the pH of a hydroponic nutrient solution fluctuates because of the unbalanced anion and cation exchange reaction with roots and there is no buffering capacity in hydroponics. The changes in the pH of a nutrient solution depend on the difference in the magnitude of nutrient uptake by plants, in terms of the balance of anions over cations. When the anions are up taken in higher concentrations than cations, for example, nitrate, the plant excretes OH− or HCO3− anions, to balance the electrical charges inside, which produces increasing in the pH value and this process is called physiological alkalinity [52]. Nutrient disorders and thereby growth reduction occur when pH is outside the optimum range, and several studies suggested that the direct effect of pH seems to be detrimental only at the extreme ends of acidity and alkalinity, and growth reductions and nutrient disorders outside of the conventional pH ranges can typically be attributed to pH-dependent factors [49, 53]. The growth response to pH is species-specific and further studies to investigate responses to pH of commercially important cultivars and species grown hydroponically need to be done [49]. In general, the pH of the plant root environment is affecting nutrient uptake, nutrient availability, ion antagonism, ionic species present, and solubility of fertilizer salts [50, 54]. Due to this, it is important to measure and maintain the pH value to the required level because a little drift in the pH value can make a lot of nutrients unavailable for the plants [47].
The availability of different nutrients at the different pH bands is indicated by the width of the white bar: The wider the bar, the more available is the nutrient. Source: Truog [
Crop | Optimum pH range |
---|---|
Tomato | 6.0–6.5 |
Pepper | 5.5–6.0 |
Egg plant | 6.0 |
Cucumber | 5.0–5.5 |
Strawberry | 6.0 |
Courgettes | 6.0 |
Banana | 5.5–6.5 |
Ficus | 5.5–6.0 |
Spinach | 6.0–7.0 |
Lettuce | 6.0–7.0 |
Cabbage | 6.5–7.0 |
Broccoli | 6.0–6.8 |
Asparagus | 6.0–8.0 |
Bean | 6.0 |
Basil | 5.5–6.0 |
Sage | 5.5–6.5 |
The optimum range of pH values for different crops grown hydroponically.
Source: Sharma et al. [48].
Precipitation/dissolution phenomena are often promoted by pH changes and, therefore, pH must be continuously controlled or buffered. Cations may form insoluble hydroxides at alkaline pH or other insoluble precipitates by reacting with other anionic nutrients. PH values above 7 may cause the precipitation of iron, zinc, copper, nickel, and manganese as insoluble hydroxides [55]. Also, at high pH values and high dissolved CO2 concentrations, macronutrients like calcium and magnesium can precipitate as carbonates. As the pH increases above 7, most of the dissolved phosphorus reacts with calcium forming calcium phosphates. Gradually, reactions occur in which the dissolved free phosphate species form insoluble compounds that cause phosphate to become unavailable [56]. According to Resh [57], slightly acidic pH is optimum for hydroponic production because iron, manganese, calcium, and magnesium may form precipitates and become unavailable at pH above 7. Bugbee [58] also reported that availability of potassium and phosphorus is slightly reduced in a nutrient solution with high pH. The reason for the reduction in phosphorus uptake at a high pH level is explained by the reduction in the concentration of H2PO4−, which is the substrate of the proton-coupled phosphate symporter in the plasma membrane, in the pH range of 5.6–8.5; conversely, a decrease in pH can increase the activity of proton-coupled solute transporters and enhance anion uptake [59]. Because pH affects nutrient availability and nutrient uptake across the plasma membrane, it is difficult to determine whether growth inhibition and nutrient disorders observed at low pH of the nutrient solution are a result of the direct effect of excessive hydronium ion concentration or pH-dependent factors affecting nutrient availability and uptake. At acidic pH, for example, in uncontrolled hydroponic systems under anoxic conditions, some elements might also precipitate as insoluble sulfides. Also, it is very important to note that, the addition of nutrients in the form of salts to hydroponic solutions may lead to hydrolysis reactions, which may result in the acidification or alkalinization of the medium. For example, nitrogen supply may alter solution pH, if nitrogen is added only in the form of NO3− (alkalinization) or NH4+ (acidification) [60].
In general, stabilizing the pH of a nutrient solution is necessary for optimum crop productivity in hydroponics [51], and maintaining an adequate nutrient solution and pH level are often cited as major obstacles to hydroponic production [61]. Despite the fact that the optimal pH in the root zone of most crops grown hydroponically ranges from 5.5 to 6.5, although values as low as 4.0 have been proposed for preventing the incidence of infections from Pythium and Phytophthora spp. [13, 49]. Low pH in the rhizosphere poses abiotic stress, resulting directly (i.e., high H+ injury of roots) or/and indirectly (i.e., limited availability of phosphorus) in restricted plant growth and crop yield. The value of pH changes as the plant absorbs nutrients from the solution, the plants give hydrogen ions into the nutrients in exchange for the ions of elements they require, and they do this to be electrically neutral. The hydrogen ions that the plants get are a result of photosynthesis. These hydrogen ions combine with water to produce hydronium ions which increases the pH of the water. This has to be counteracted by adding acids like phosphoric acid into the nutrient solution to ensure the solubility of all the elements in the nutrients [47]. Various acids or bases used to adjust pH may also provide some interacting factors on the plants. For example, potassium hydroxide, sodium hydroxide, phosphoric acid, and acetic acid are commonly used to maintain the pH of the nutrient solution. The presence of these acids or bases may have had small impacts through the addition of minerals such as potassium, phosphorus, and/or sodium and the increased concentration of acetates. Other nonmineral nutrients containing acids (carbonic, formic, citric, acetylsalicylic, etc.) could be used for pH adjustment, but their potential toxicity and interactions with the nutrient solutions would need careful consideration and study. Overall, it would be ideal to have a solution where pH could be maintained easily within a small pH range without the addition of mineral nutrients [62]. Wang et al. [63] found that a mixture of three (HNO3, H3PO4, and H2SO4) acids was much more effective than only single acid for maintaining an optimal solution pH of 5.5–6.5. The management of nutrient solution pH is an important challenge in soilless systems, since not only it may determine plant growth but also it influences dry matter production, root rhizosphere, and apoplastic pH [13]. However, in soilless culture, when maintaining marginal values of the optimum pH range, the risk of exceeding or dropping below them for some time increases due to the limited volume of nutrient solution per plant that is available in the root zone, and most plants, when exposed to external pH levels >7 or < 5, show growth restrictions. When soilless substrates are used instead of liquid-based hydroponics, pH in the nutrient solution interacts with substrates [64], and micronutrient toxicity occurs rather than deficiency. Therefore, the evaluation of the plant’s pH response must consider the growing systems employed.
In soilless culture, the total salt concentration of a nutrient solution must be considered, and the nutrient solution EC is an index of salt concentration and an indicator of electrolyte concentration of the solution and is related to the number of ions available to plants in the root zone. The EC is a measure of the total salts dissolved in the hydroponic nutrient solution. It is used for monitoring applications of fertilizers. However, EC reading does not provide information regarding the exact mineral content of the nutrient solution. It is an important factor that reflects the total content of macro- and micro-elements available to plants [6], and it is an easy and accurate method of measuring total salt concentration. Inadequate management of the nutrient solution, such as the use of a too high or a too low concentration of the nutrient solution, or an imbalanced ion composition could inhibit plant growth due to either toxicity or nutrient-induced deficiency [65]. In hydroponic production systems, EC management is one of the most important and manageable cultural practices that affects the visual, nutritional, and phytochemical quality of leafy vegetables [4]. However, managing the EC in moderately high levels—either by using low-quality water that contains residual ions, such as Cl−, Na+ and SO4−, or by adding major nutrients through stock solutions—is a cultivation management technique that provides great potential to achieve high dietary and organoleptic quality in fresh vegetables [24]. Each plant species has a proper uptake rate of the nutrient solution; excessively high or low levels of the nutrient solution have a negative effect on plants. For many leafy vegetables, there are already specific formulations used on a commercial scale for hydroponics, and the optimum EC levels for different crops grown hydroponically are shown in Table 4. Although the plants were supplied with suitable ion ratios, plants can easily suffer from nutrient deficiency or excess if the nutrient solution concentration is low or high. Therefore, it is crucial to determine the suitable EC level of nutrient solutions with favorable ion ratios for growing plants [6]. The optimal EC is crop specific and depends on environmental conditions [66]. Thus, the determination of the most favorable nutrient ratio for each species under diverse climatic conditions is of major importance.
Crop | EC (dSm−1) |
---|---|
Tomato | 2.0–4.0 |
Pepper | 0.8–1.8 |
Egg plant | 2.5–3.5 |
Cucumber | 1.7–2.0 |
Strawberry | 1.8–2.2 |
Courgettes | 1.8–2.4 |
Banana | 1.8–2.2 |
Ficus | 1.6–2.4 |
Spinach | 1.8–2.3 |
Lettuce | 1.2–1.8 |
Cabbage | 2.5–3.0 |
Broccoli | 2.8–3.5 |
Asparagus | 1.4–1.8 |
Bean | 2.0–4.0 |
Basil | 1.0–1.6 |
Sage | 1.0–1.6 |
Optimum range of EC values for different crops grown hydroponically.
Source: Sharma et al. [48].
Many studies have reported that EC levels of nutrient solutions affect the growth of various crops. The optimal EC level range should be from 1.5 to 3.5 dS m−1 for most hydroponic crops, but this value varies between crop species and phenological stages [6]. However, the upper levels of EC in nutrient solutions must be considered for each species, since excessive EC values may decrease the osmotic potential of the nutrient solution and consequently result in delays in water transport from roots to fruits, with negative effects on fruit expansion and yield [24]. The EC levels showed a considerable influence on the ratio of ions as well as the uptake content of individual minerals. Too low and too high EC would reduce yields, visual quality, phytochemical compounds and lead to a less attractive color and taste to consumers, and enhance the negative health effects due to nitrate accumulation [4]. Increasing conductivity in nutrient solution may reduce water absorption by plants and decrease photosynthesis [67]. Also, higher EC means plants are exposed to salinity stress and high levels of nutrients, which hinders nutrient uptake and induces osmotic stress, ion toxicity, nutrient imbalance, wastes nutrients, and increases the discharge of more nutrients into the environment, resulting in environmental pollution. At the extreme EC level, plants are not able to take up any more water, and water will move backward out of the nutrient solution, which makes plants withered. The elevated EC may have negative effects on yield but can also positively affect the quality of the fresh produce, thus compromising any yield losses through the production of products with a high added value [24]. As an example, the yield of tomatoes under the hydroponic system increased as EC of the nutrient solution increased from 0 to 3 dS m−1 and decreased as the EC increased from 3 to 5 dS m−1 due to an increase in water stress [68]. Lower EC values mean the supply of some nutrients to the crop may be inadequate are mostly accompanied by nutrient deficiencies and decreasing yield [69]. So, appropriate management of EC in hydroponics technique can give an effective tool for improving vegetable yield and quality [48].
EC is modified by plants as they absorb nutrients and water from the nutrient solution. When a nutrient solution is applied continuously, plants can uptake ions at very low concentrations, and a high proportion of the nutrients are not used by plants. However, in particular situations, too low concentrations do not cover the minimum demand for certain nutrients. On the other hand, high concentrated nutrient solutions lead to excessive nutrient uptake and therefore toxic effects may be expected. Therefore, a decrease in the concentration of some ions and an increase in the concentration of others is observed simultaneously, both in close and open systems. It was observed, in a closed hydroponic system with a rose crop, that the concentration of iron decreased very fast, while that of Ca2+, Mg2+, and Cl− increased; moreover, concentrations of K+, Ca2+, and SO42− did not reach critical levels [70]. Providing the most suitable nutrient solution and EC level for growing vegetables and crops in hydroponic systems helps to avoid the waste of nutrient solution, which contributes to saving production costs for growing crops in plant factories and preventing environmental pollution, and the value of EC is required to be controlled to ensure nutritional elements needed by plants is fulfilled.
Nutrient solution temperature is considered as one of the most important determining factors of crop yield and quality in hydroponic production systems [71]. The temperature of the nutrient solution affects the physiological process in the root, such as the absorption of water and nutrients, and the thermal regulation of hydroponic solution can contribute to improving and optimizing plant physiological processes [72]. Nutrient uptake for plants grown in glasshouses may be positively and adversely affected by manipulating the hydroponic solution temperature to the optimum level [73]. It is also possible that the increased temperature facilitated solubility of minerals and increase uptake since the rate of dissolving of solutes increases with increase in temperature [74], and the nutrient solution temperature tends to determine the concentration of nutrients absorbed by the plant, as more nutrients are dissolved at higher temperatures and less at lower temperatures, consequently influencing the efficiency of the photosynthetic apparatus [75]. Calatayud et al. [76] revealed that, in most plant species, nutrient uptake by roots decreased at low temperatures. Increasing nutrient temperatures increased nutrient uptake in cucumber and enhanced plant growth leading to a significant increase in yield [77]. The uptake rate of N, P, K, Na, Fe, Mn, and Zn in Jojoba was significantly reduced at low temperatures [78]. While, in cucumber, uptake of N, P, K, Ca, and Mg was increased when the temperature was raised in a closed hydroponic system from 12 to 20°C [77]. It has been reported that commercial growers experience a lower level of ornamental plant production in winter than in summer due to the low temperature of the solution [79, 80]. Also, the production of various plant metabolites is influenced by the temperature of the root zone in many plants, including leafy vegetables [67].
The chemical equilibrium of the solution is affected by nutrient temperature, and this is particularly crucial for areas where the over warming of the nutrient solution often occurs, impacting also all the physiological processes in the plant [81]. Generally, the cold solution increased NO3− uptake and thin-white roots production but decreased water uptake and it also influenced the photosynthetic apparatus. The temperature of the nutrient solution also has a direct relation to the amount of oxygen consumed by plants, and an inverse relation to the oxygen dissolved. It is of paramount importance to regulate hydroponic solution temperatures in situations whereby, plants are grown in a controlled environment during winter months. Optimizing solution temperature can be achieved by warming the nutrient solution and this showed success in a variety of crops [82, 83]. High temperature in the root zone is one of the most significant limiting factors for lettuce cultivation in tropical hydroponics. Instead of cooling the entire greenhouse air, the root zone cooling system could be an energy-efficient cooling system for a greenhouse for tropical hydroponics. Therefore, it is very important to study the optimum nutrient temperature requirements for different crops grown in climates with adverse winter conditions.
Maintaining enough dissolved O2 in a nutrient solution in a hydroponic system is crucial for plant health. Oxygen availability to roots grown in soilless culture can become limiting in case O2 demand exceeds O2 supply, inducing a reduction in root growth rate, ion, and water uptake, eventually reducing plant production [84]. Plants grown in hydroponic systems can quickly deplete the dissolved O2 in the nutrient solution resulting in poor root aeration, especially when greenhouse temperatures are high, Table 5 shows O2 solubility in pure water at different temperatures. Jong et al. [86] noticed that cucumber growth was significantly affected by root-zone aeration. Roosta et al. [87] found improve eggplant growth with rising O2 levels in the nutrient solution in floating hydroponic cultures and higher O2 levels seemed to alleviate signs of ammonium toxicity among the tested plants. Root respiration also decreases when O2 supply in the root environment falls below a critical O2 concentration [88]. The sensitivity of roots to low O2 concentration depends on its effect on mitochondrial respiration because it supplies most of the energy required for root function. Reduction in O2 levels in the nutrient solution could lead to poor roots, an increase in the incidence of diseases and pests, and a reduction in plant growth. Oxygen around a plant’s roots affects the beneficial microorganisms that provide protection from pathogens and improve nutrient uptake. Tomato plant roots would be much more susceptible to Pythium infection if root zone O2 dropped below 2.8 mg/L [89]. Dissolved O2 concentration, is strongly dependent on solution temperature and flow rate near the root zone, as well as on the growth rate of the crop, and may be influenced by the bacterial community present in the solution. The temperature has a direct relationship to the amount of oxygen consumed by the plant and a reverse relationship with dissolved oxygen from the nutrient solution. The consumption of O2 increases when the temperature of the nutrient solution increases. Consequently, it produces an increase in the relative concentration of CO2 in the root environment if the root aeration is not adequate [90]. For overcoming the limited oxygen exchange between the atmosphere and the nutrient solution in static deep water culture hydroponics, the nutrient solution is aerated by an air bubbler connecting with the pump to provide adequate root oxygenation [6]. Roots of loose-leaf lettuce grown in a floating raft hydroponic system were found to have a better condition with oxygen enrichment done in nutrient solution up to aeration pressure of 0.012 mPa and concentration of 600 ppm, with indicators of increasing length and total root surface area [68]. So, it is important to make sure the nutrient solution is properly aerated to maintain enough oxygen for the plant cells found in the root mass since this is crucial to the function of the plant’s cells and the microbial world.
Temperature (oC) | Oxygen solubility (mg L−1) |
---|---|
10 | 11.29 |
15 | 10.08 |
20 | 9.09 |
25 | 8.26 |
30 | 7.56 |
35 | 6.95 |
40 | 6.41 |
45 | 5.93 |
Solubility of oxygen in pure water at various temperatures.
Source: Trejo-Téllez et al. [85].
An optimized and well-balanced supply of nutrients is a prerequisite for efficient use of the resources by hydroponically grown vegetables, not only to ensure a high yield but also to guarantee the quality of the edible tissues. In hydroponics, because of the limited nutrient-buffering capacity of the system and the ability to make rapid changes, careful monitoring of the system is necessary. The frequency and volume of the nutrient solution applied depends on the type of substrate, the crop and growth stage, the size of the container, the irrigation systems used, and the prevailing climatic conditions. Depending on the stage of plant development, some elements in the nutrient solution will be depleted more quickly than others and as water evaporates from the nutrient solution, the fertilizer becomes more concentrated and can burn plant roots. In hydroponics, nutrient management is very important and must be done as highly efficient as possible to improve productivity without harming the environment. Nutrient management included- application the right fertilizers source (e.g., ammonium or nitrate as nitrogen source), balanced nutrient solution according to plants needs and according to plant growth stages and climatic conditions. The main principle of crop nutrient management is to prevent overapplication of nutrients, which prevents loss due to low yield from toxicities of some nutrients resulting from the unnecessary use of fertilizers. It was reported that the strong difference between the ion ratios presented in the nutrient solution and those absorbed by plants led to the accumulation of certain ions in the nutrient solution, which caused an imbalance of mineral elements in the nutrient solution and created more energy to absorb the suitable ions [6]. Recycling exhausted solutions may also represent an efficient strategy to prevent groundwater and environmental pollution. However, the main problem with the reuse of exhausted nutrient solutions is the shortage of some key macro and micronutrients [91] and their increased salinity [92] causing, in turn, problems for crops [93, 94]. Thus, it is very important to develop management practices/tools that reduce salinity in recycled solutions and/or minimize the physiological impact of salinity on plants. The salinity increase could be contrasted by treating the recycled water with appropriate osmotic systems, including forward and reverse osmosis.
In closed hydroponic systems, accumulation of potentially toxic organic compounds released by the roots of cultivated plants may occur and to overcome this issue, several treatment techniques have been proposed for root exudates degradation or removal. However, for the treatment to be effective, it should be able to remove root exudates without interfering with the inorganic mineral nutrients in the solution. As above-mentioned, the regulation of the solution flow rate in hydroponic production affects plant growth, which in turn affects crop yield and quality. The influence of nutrient solution flow rate on plant growth is related to the plants’ physical environment. The flow of nutrient solution not only promotes nutrient ion diffusion but also increases the kinetic energy available to plant roots Therefore, adjusting the flow rate can improve plant yield and a reasonable flow pattern must be carefully selected. Because increasing the flow means increasing electricity consumption, it increases the cost of operation. Therefore, it is important to balance plant yield, nutrient management, and energy utilization. According to Baiyin et al. [95], determining the ideal flow rate for hydroponic production may help to increase yield. However, such a determination requires a specific analysis of each crop and growing environment. The hydroponic nutrient solution is the sole source of nutrients to the plant; therefore, it is imperative that a balanced solution, containing all the right plant nutrients, is applied.
Hydroponic cultivation is revolutionizing agricultural crop production techniques all over the world owing to its minimal environmental footprint, enhanced pest control, and provide high crop yield. It allows more accurate control of environmental conditions that offer possibilities for increasing production and improving the quality of crops. The rapid development of computers and controllers has enabled the opportunity to apply the controller in hydroponics. The microcontroller could be used to control these nutrient solution parameters by using relevant sensors. It monitors the conductivity and pH throughout 24 h during the whole cycle of production. Also, it helps in monitoring temperature, nutrient atomization, EC, and pH fluctuations and level of nutrient solution in the nutrient reservoir. However, although the comprehension of the multi-level interactions among the various mineral elements is considered crucial to understanding the different sensing and signaling pathways induced by a single or multiple shortage/s, the impact of these nutrients’ interactions on crop performance is largely unknown [32].
Some hydroponic growers use more than the required amounts of nutrients for crop growth to minimize the chances of nutrient deficiency. But one of the most important factors for a successful hydroponics system is the use of the appropriate nutrient solution, and it is important to control the amount of nutrients to allow or deny plants the nutrient accumulation. While hydroponic systems are considered to represent a sustainable method for growing plants, the nutrient solution used in hydroponic systems is based on chemical fertilizers which are mined from scarce and non-renewable resources. Recently, there has been an increased interest in organic hydroponics, as the market for organic food continues to grow and some studies have reported the possibility of growing vegetables using an organic nutrient solution. For optimizing the utilization of organic waste for hydroponic plant growth, a solubilization step is required to break down organic matter and mobilize nutrients [96]. For example, the direct use of organic fertilizers in hydroponic systems may inhibit plant growth due to the high biological oxygen demand in the root zone caused by the presence of dissolved organic carbon compounds. Additionally, most of the nutrients in organic sources, such as waste material from the agricultural and aquacultural industry, are not in ionic forms and, hence, are not directly available for plants. Also, the last decade has seen increasing interest in using wastewater as a source of hydroponic nutrition. This aims at a dual benefit of optimizing water reuse as well as a practical end point for wastewater management. Untreated domestic rinse water obtained from washing machine second-cycle rinse can effectively be used for indoor hydroponic cultivation of plants without the need for any additional fertilizer. It also entails the benefits of significant savings in water use, sewage disposal, ecosystem protection, and the possibility to produce economically viable food crops [97]. Nowadays, hydroponic farming technology is extensively used in producing ornamental plants and flowers. Controllable application of fertilizers, the ability to change nutrients in different weather conditions and different plant growth stages, reduction of fertilizer leaching from the root zone, reduction of contamination, environmental protection, and enhancement of the quality and quantity of products are becoming some of the advantages of this technology.
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Muedi",authors:[{id:"225304",title:"Dr.",name:"Vhahangwele",middleName:null,surname:"Masindi",slug:"vhahangwele-masindi",fullName:"Vhahangwele Masindi"},{id:"241403",title:"M.Sc.",name:"Khathutshelo",middleName:"Lilith",surname:"Muedi",slug:"khathutshelo-muedi",fullName:"Khathutshelo Muedi"}]},{id:"37067",doi:"10.5772/35482",title:"Fourier Transform Infrared Spectroscopy for Natural Fibres",slug:"fourier-transform-infrared-spectroscopy-for-natural-fibres",totalDownloads:9291,totalCrossrefCites:167,totalDimensionsCites:400,abstract:null,book:{id:"2270",slug:"fourier-transform-materials-analysis",title:"Fourier Transform",fullTitle:"Fourier Transform - Materials Analysis"},signatures:"Mizi Fan, Dasong Dai and Biao Huang",authors:[{id:"104647",title:"Prof.",name:"Mizi",middleName:null,surname:"Fan",slug:"mizi-fan",fullName:"Mizi Fan"}]},{id:"36171",doi:"10.5772/36942",title:"Research of Calcium Phosphates Using Fourier Transform Infrared Spectroscopy",slug:"research-of-calcium-phosphates-using-fourier-transformation-infrared-spectroscopy",totalDownloads:9277,totalCrossrefCites:132,totalDimensionsCites:381,abstract:null,book:{id:"1591",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",title:"Infrared Spectroscopy",fullTitle:"Infrared Spectroscopy - Materials Science, Engineering and Technology"},signatures:"Liga Berzina-Cimdina and Natalija Borodajenko",authors:[{id:"110522",title:"Prof.",name:"Liga",middleName:null,surname:"Berzina-Cimdina",slug:"liga-berzina-cimdina",fullName:"Liga Berzina-Cimdina"},{id:"112181",title:"MSc.",name:"Natalija",middleName:null,surname:"Borodajenko",slug:"natalija-borodajenko",fullName:"Natalija Borodajenko"}]},{id:"41411",doi:"10.5772/53659",title:"Textile Dyes: Dyeing Process and Environmental Impact",slug:"textile-dyes-dyeing-process-and-environmental-impact",totalDownloads:20684,totalCrossrefCites:101,totalDimensionsCites:320,abstract:null,book:{id:"3137",slug:"eco-friendly-textile-dyeing-and-finishing",title:"Eco-Friendly Textile Dyeing and Finishing",fullTitle:"Eco-Friendly Textile Dyeing and Finishing"},signatures:"Farah Maria Drumond Chequer, Gisele Augusto Rodrigues de Oliveira, Elisa Raquel Anastácio Ferraz, Juliano Carvalho Cardoso, Maria Valnice Boldrin Zanoni and Danielle Palma de Oliveira",authors:[{id:"49040",title:"Prof.",name:"Danielle",middleName:null,surname:"Palma De Oliveira",slug:"danielle-palma-de-oliveira",fullName:"Danielle Palma De Oliveira"},{id:"149074",title:"Prof.",name:"Maria Valnice",middleName:null,surname:"Zanoni",slug:"maria-valnice-zanoni",fullName:"Maria Valnice Zanoni"},{id:"153502",title:"Ph.D.",name:"Farah",middleName:null,surname:"Chequer",slug:"farah-chequer",fullName:"Farah Chequer"},{id:"153504",title:"MSc.",name:"Gisele",middleName:null,surname:"Oliveira",slug:"gisele-oliveira",fullName:"Gisele Oliveira"},{id:"163377",title:"Dr.",name:"Juliano",middleName:null,surname:"Cardoso",slug:"juliano-cardoso",fullName:"Juliano Cardoso"},{id:"163393",title:"Dr.",name:"Elisa",middleName:null,surname:"Ferraz",slug:"elisa-ferraz",fullName:"Elisa Ferraz"}]},{id:"17237",doi:"10.5772/24553",title:"Hydrogels: Methods of Preparation, Characterisation and Applications",slug:"hydrogels-methods-of-preparation-characterisation-and-applications",totalDownloads:65928,totalCrossrefCites:87,totalDimensionsCites:279,abstract:null,book:{id:"248",slug:"progress-in-molecular-and-environmental-bioengineering-from-analysis-and-modeling-to-technology-applications",title:"Progress in Molecular and Environmental Bioengineering",fullTitle:"Progress in Molecular and Environmental Bioengineering - From Analysis and Modeling to Technology Applications"},signatures:"Syed K. H. Gulrez, Saphwan Al-Assaf and Glyn O Phillips",authors:[{id:"58120",title:"Prof.",name:"Saphwan",middleName:null,surname:"Al-Assaf",slug:"saphwan-al-assaf",fullName:"Saphwan Al-Assaf"}]}],mostDownloadedChaptersLast30Days:[{id:"35255",title:"Mechanical Transmissions Parameter Modelling",slug:"mechanical-transmissions-parameter-modelling",totalDownloads:7442,totalCrossrefCites:1,totalDimensionsCites:2,abstract:null,book:{id:"1982",slug:"mechanical-engineering",title:"Mechanical Engineering",fullTitle:"Mechanical Engineering"},signatures:"Isad Saric, Nedzad Repcic and Adil Muminovic",authors:[{id:"101313",title:"Prof.",name:"Isad",middleName:null,surname:"Saric",slug:"isad-saric",fullName:"Isad Saric"}]},{id:"68505",title:"Research Design and Methodology",slug:"research-design-and-methodology",totalDownloads:25128,totalCrossrefCites:9,totalDimensionsCites:18,abstract:"There are a number of approaches used in this research method design. The purpose of this chapter is to design the methodology of the research approach through mixed types of research techniques. The research approach also supports the researcher on how to come across the research result findings. In this chapter, the general design of the research and the methods used for data collection are explained in detail. It includes three main parts. The first part gives a highlight about the dissertation design. The second part discusses about qualitative and quantitative data collection methods. The last part illustrates the general research framework. The purpose of this section is to indicate how the research was conducted throughout the study periods.",book:{id:"8511",slug:"cyberspace",title:"Cyberspace",fullTitle:"Cyberspace"},signatures:"Kassu Jilcha Sileyew",authors:[{id:"292841",title:"Ph.D.",name:"Kassu",middleName:null,surname:"Jilcha Sileyew",slug:"kassu-jilcha-sileyew",fullName:"Kassu Jilcha Sileyew"}]},{id:"67558",title:"Polymerase Chain Reaction (PCR): Principle and Applications",slug:"polymerase-chain-reaction-pcr-principle-and-applications",totalDownloads:10667,totalCrossrefCites:8,totalDimensionsCites:18,abstract:"The characterization of the diversity of species living within ecosystems is of major scientific interest to understand the functioning of these ecosystems. It is also becoming a societal issue since it is necessary to implement the conservation or even the restoration of biodiversity. Historically, species have been described and characterized on the basis of morphological criteria, which are closely linked by environmental conditions or which find their limits especially in groups where they are difficult to access, as is the case for many species of microorganisms. The need to understand the molecular mechanisms in species has made the PCR an indispensable tool for understanding the functioning of these biological systems. A number of markers are now available to detect nuclear DNA polymorphisms. In genetic diversity studies, the most frequently used markers are microsatellites. The study of biological complexity is a new frontier that requires high-throughput molecular technology, high speed computer memory, new approaches to data analysis, and the integration of interdisciplinary skills.",book:{id:"7728",slug:"synthetic-biology-new-interdisciplinary-science",title:"Synthetic Biology",fullTitle:"Synthetic Biology - New Interdisciplinary Science"},signatures:"Karim Kadri",authors:[{id:"290766",title:"Dr.",name:"Kadri",middleName:null,surname:"Karim",slug:"kadri-karim",fullName:"Kadri Karim"}]},{id:"62059",title:"Types of HVAC Systems",slug:"types-of-hvac-systems",totalDownloads:12438,totalCrossrefCites:8,totalDimensionsCites:14,abstract:"HVAC systems are milestones of building mechanical systems that provide thermal comfort for occupants accompanied with indoor air quality. HVAC systems can be classified into central and local systems according to multiple zones, location, and distribution. Primary HVAC equipment includes heating equipment, ventilation equipment, and cooling or air-conditioning equipment. Central HVAC systems locate away from buildings in a central equipment room and deliver the conditioned air by a delivery ductwork system. Central HVAC systems contain all-air, air-water, all-water systems. Two systems should be considered as central such as heating and cooling panels and water-source heat pumps. Local HVAC systems can be located inside a conditioned zone or adjacent to it and no requirement for ductwork. Local systems include local heating, local air-conditioning, local ventilation, and split systems.",book:{id:"6807",slug:"hvac-system",title:"HVAC System",fullTitle:"HVAC System"},signatures:"Shaimaa Seyam",authors:[{id:"247650",title:"M.Sc.",name:"Shaimaa",middleName:null,surname:"Seyam",slug:"shaimaa-seyam",fullName:"Shaimaa Seyam"},{id:"257733",title:"MSc.",name:"Shaimaa",middleName:null,surname:"Seyam",slug:"shaimaa-seyam",fullName:"Shaimaa Seyam"},{id:"395618",title:"Dr.",name:"Shaimaa",middleName:null,surname:"Seyam",slug:"shaimaa-seyam",fullName:"Shaimaa Seyam"}]},{id:"70315",title:"Some Basic and Key Issues of Switched-Reluctance Machine Systems",slug:"some-basic-and-key-issues-of-switched-reluctance-machine-systems",totalDownloads:1264,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"Although switched-reluctance machine (SRM) possesses many structural advantages and application potential, it is rather difficult to successfully control with high performance being comparable to other machines. Many critical affairs must be properly treated to obtain the improved operating characteristics. This chapter presents the basic and key technologies of switched-reluctance machine in motor and generator operations. The contents in this chapter include: (1) structures and governing equations of SRM; (2) some commonly used SRM converters; (3) estimation of key parameters and performance evaluation of SRM drive; (4) commutation scheme, current control scheme, and speed control scheme of SRM drive; (5) some commonly used front-end converters and their operation controls for SRM drive; (6) reversible and regenerative braking operation controls for SRM drive; (7) some tuning issues for SRM drive; (8) operation control and some tuning issues of switched-reluctance generators; and (9) experimental application exploration for SRM systems—(a) wind generator and microgrid and (b) EV SRM drive.",book:{id:"8899",slug:"modelling-and-control-of-switched-reluctance-machines",title:"Modelling and Control of Switched Reluctance Machines",fullTitle:"Modelling and Control of Switched Reluctance Machines"},signatures:"Chang-Ming Liaw, Min-Ze Lu, Ping-Hong Jhou and Kuan-Yu Chou",authors:[{id:"37616",title:"Prof.",name:"Chang-Ming",middleName:null,surname:"Liaw",slug:"chang-ming-liaw",fullName:"Chang-Ming Liaw"},{id:"306461",title:"Mr.",name:"Min-Ze",middleName:null,surname:"Lu",slug:"min-ze-lu",fullName:"Min-Ze Lu"},{id:"306463",title:"Mr.",name:"Ping-Hong",middleName:null,surname:"Jhou",slug:"ping-hong-jhou",fullName:"Ping-Hong Jhou"},{id:"306464",title:"Mr.",name:"Kuan-Yu",middleName:null,surname:"Chou",slug:"kuan-yu-chou",fullName:"Kuan-Yu Chou"}]}],onlineFirstChaptersFilter:{topicId:"1",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82713",title:"Fouling and Mechanism",slug:"fouling-and-mechanism",totalDownloads:1,totalDimensionsCites:null,doi:"10.5772/intechopen.105878",abstract:"Fouling is the deposition of material on the heat transfer surface which reduces the film heat transfer coefficient. The impact of fouling on the heat exchanger is manifested as the reduction of thermal and hydraulic performance, in which the latter has a minor effect. This factor needs to be considered when calculating the effectiveness of the heat exchanger. During the design of heat exchangers, the fouling factor increases the required heat transfer area, which adds extra manufacturing costs. With less efficient heat exchangers, the economic cost of fouling is related to excess fuel consumption, loss of production, and maintenance or cleaning. The extra fuel consumption also damages the environment by increasing greenhouse gas production. Although much of the research work has been done on modeling and predicting fouling, it is still a poorly understood phenomenon representing the complexity of its mechanism. The common fouling mitigation action after the onset of fouling is to optimize the operating condition, e.g., increase the bulk flow velocity or decrease surface temperature. However, many quantitative and semi-empirical models have been developed to predict the fouling rate for preventive actions and optimizing cleaning schedules.",book:{id:"11161",title:"Heat Transfer",coverURL:"https://cdn.intechopen.com/books/images_new/11161.jpg"},signatures:"Obaid ur Rehman, Nor Erniza Mohammad Rozali and Marappa Gounder Ramasamy"},{id:"83057",title:"Communication Technologies and Their Contribution to Sustainable Smart Cities",slug:"communication-technologies-and-their-contribution-to-sustainable-smart-cities",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106223",abstract:"Sustainable smart cities (SSC) are becoming a reality as many develop their unique model of smart cities based on vast communication infrastructure. New technologies led to innovative ecosystems where transportation, logistics, maintenance, etc., are automated and accessed remotely. Information and communication coordinate their overall activities. Sensors embedded in these devices sense the environment to provide the required input. Together with artificial intelligence, machine learning, and deep learning, it enables them to facilitate effective decision-making. This chapter discusses the role of integrating technologies in smart cities, focusing on the information and communication aspects, challenges, limitations, and mitigation strategies related to the infrastructure, implementations, and best practices for attaining SSC. We propose a four-layered model covering the main aspects of incorporating communication technology within sustainable smart cities. It covers the basic physical level, providing guidelines for designing a smart city that supports the requirements of a proper communications infrastructure. The level above is the network level where we describe current communication networks and technologies. The rest two upper layers represent the software with integrated and embedded communication components. In summary, we conclude that communication technology is the key enabler of most of the activities performed in smart cities.",book:{id:"11507",title:"New Generation of Sustainable Smart Cities",coverURL:"https://cdn.intechopen.com/books/images_new/11507.jpg"},signatures:"Menachem Domb"},{id:"83055",title:"Boron Clusters in Biomedical Applications: A Theoretical Viewpoint",slug:"boron-clusters-in-biomedical-applications-a-theoretical-viewpoint",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106215",abstract:"In this chapter, we presented an analysis of the recent advances in the applications of boron clusters in biomedical fields such as the development of biosensors and drug delivery systems on the basis of quantum chemical calculations. Biosensors play an essential role in many sectors, e.g., law enforcement agencies for sensing illicit drugs, medical communities for detecting overdosed medications from human and animal bodies, etc. The drug delivery systems have theoretically been proposed for many years and subsequently implemented by experiments to deliver the drug to the targeted sites by reducing the harmful side effects significantly. Boron clusters form a rich and colorful family of atomic clusters due to their unconventional structures and bonding phenomena. Boron clusters and their complexes have various biological activities such as the drug delivery, imaging for diagnosis, treatment of cancer, and probe of protein-biomolecular interactions. For all of these reactivities, the interaction mechanisms and the corresponding energetics between biomaterials and boron clusters are of essential importance as a basic step in the understanding, and thereby design of relevant materials. During the past few years, attempts have been made to probe the nature of these interactions using quantum chemical calculations mainly with density functional theory (DFT) methods. This chapter provides a summary of the theoretical viewpoint on this issue.",book:{id:"11762",title:"Characteristics and Applications of Boron",coverURL:"https://cdn.intechopen.com/books/images_new/11762.jpg"},signatures:"Ehsan Shakerzadeh, Elham Tahmasebi, Long Van Duong and Minh Tho Nguyen"},{id:"83061",title:"Dipole Solitons in a Nonlocal Nonlinear Medium with Self-Focusing and Self-Defocusing Quintic Nonlinear Responses",slug:"dipole-solitons-in-a-nonlocal-nonlinear-medium-with-self-focusing-and-self-defocusing-quintic-nonlin",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106207",abstract:"Stability dynamics of dipole solitons have been numerically investigated in a nonlocal nonlinear medium with self-focusing and self-defocusing quintic nonlinearity by the squared-operator method. It has been demonstrated that solitons can stay nonlinearly stable for a wide range of each parameter, and two nonlinearly stable regions have been found for dipole solitons in the gap domain. Moreover, it has been observed that instability of dipole solitons can be improved or suppressed by modification of the potential depth and strong anisotropy coefficient.",book:{id:"10958",title:"Vortex Dynamics - From Physical to Mathematical Aspects",coverURL:"https://cdn.intechopen.com/books/images_new/10958.jpg"},signatures:"Mahmut Bağcı, Melis Turgut, Nalan Antar and İlkay Bakırtaş"},{id:"82984",title:"Feedback Linearization Control of Interleaved Boost Converter Fed by PV Array",slug:"feedback-linearization-control-of-interleaved-boost-converter-fed-by-pv-array",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106355",abstract:"One of the powerful methods of nonlinear control is the feedback linearization technique. This technique consists of input state and input-output linearization methods. In this chapter, the feedback linearization technique, including input state and input-output linearization methods, is described. Then, input-output linearization method is used for output voltage control of interleaved boost converter. Firstly, mathematical model of the interleaved boost converter is derived after that the method is applied. Besides, the interleaved boost converter is fed by a PV array under irradiation level and ambient temperature change. As a result of the simulation study, output voltage control of interleaved boost converter under reference voltage change is realized as desired.",book:{id:"11499",title:"Nonlinear Systems - Recent Developments and Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11499.jpg"},signatures:"Erdal Şehirli"},{id:"1082338",title:"Capacitated Clustering Models to Real Life Applications",slug:null,totalDownloads:1,totalDimensionsCites:0,doi:"10.5992/intechopen.1000213",abstract:'This chapter considers the use of different capacitated clustering problems and models that fits better in real-life applications such as household waste collection, IT teams layout in software factories, wholesales distribution, and staff’s home collection or delivery to/from workplace. Each application is explored in its regular form as it is being developed by contractors and/or users. We consider for each application the aspects of solving the problem by the appropriate mathematical programming model and decision support methodology (using aggregated Geographical Information System and mobile technology) to hold correctly and most precisely the problems and difficulties related to instances in evaluation. The experience on these fields is here revealed in detailed form as the results obtained by using the techniques here explained.
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The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"3",title:"Bacterial Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",isOpenForSubmission:!0,editor:{id:"205604",title:"Dr.",name:"Tomas",middleName:null,surname:"Jarzembowski",slug:"tomas-jarzembowski",fullName:"Tomas Jarzembowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKriQAG/Profile_Picture_2022-06-16T11:01:31.jpg",biography:"Tomasz Jarzembowski was born in 1968 in Gdansk, Poland. He obtained his Ph.D. degree in 2000 from the Medical University of Gdańsk (UG). After specialization in clinical microbiology in 2003, he started studying biofilm formation and antibiotic resistance at the single-cell level. In 2015, he obtained his D.Sc. degree. His later study in cooperation with experts in nephrology and immunology resulted in the designation of the new diagnostic method of UTI, patented in 2017. He is currently working at the Department of Microbiology, Medical University of Gdańsk (GUMed), Poland. Since many years, he is a member of steering committee of Gdańsk branch of Polish Society of Microbiologists, a member of ESCMID. 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Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University. His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:{name:"Medical University Plovdiv",country:{name:"Bulgaria"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. RELACION DE PONENCIAS DE LA SOCIEDAD ESPAÑOLA DE OFTALMOLOGIA. 10/2014.",institutionString:null,institution:null},{id:"243698",title:"Dr.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:null,institution:null},{id:"7227",title:"Dr.",name:"Hiroaki",middleName:null,surname:"Matsui",slug:"hiroaki-matsui",fullName:"Hiroaki Matsui",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Tokyo",country:{name:"Japan"}}},{id:"312999",title:"Dr.",name:"Bernard O.",middleName:null,surname:"Asimeng",slug:"bernard-o.-asimeng",fullName:"Bernard O. Asimeng",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Ghana",country:{name:"Ghana"}}},{id:"318905",title:"Prof.",name:"Elvis",middleName:"Kwason",surname:"Tiburu",slug:"elvis-tiburu",fullName:"Elvis Tiburu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Ghana",country:{name:"Ghana"}}},{id:"336193",title:"Dr.",name:"Abdullah",middleName:null,surname:"Alamoudi",slug:"abdullah-alamoudi",fullName:"Abdullah Alamoudi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"318657",title:"MSc.",name:"Isabell",middleName:null,surname:"Steuding",slug:"isabell-steuding",fullName:"Isabell Steuding",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}},{id:"318656",title:"BSc.",name:"Peter",middleName:null,surname:"Kußmann",slug:"peter-kussmann",fullName:"Peter Kußmann",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}}]}},subseries:{item:{id:"88",type:"subseries",title:"Marketing",keywords:"Consumer Trends, Consumer Needs, Media, Pricing, Distribution, Branding, Innovation, Neuromarketing",scope:"