List of SAR systems.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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He received his MSc (2001) and PhD (2006) degrees in Electrical and Electronics Engineering from the Universitat Politècnica de València, Spain. He is also Ingénieur Supélec (2001), from the École Supérieure d’Électricité, France. Since 2004 he is Associate Professor of Electronics and Medical Imaging in the Universitat Politècnica de València (Spain) and, in 2008 he joined the Center for Biomaterials and Tissue Engineering of the same University. 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Two major analyses are conducted in the present research. In the first, the characteristic of heat affected zone (HAZ) and free air ball (FAB) on ultra thin Au wire have been carefully experimental measured. Secondary, the dynamic response on Al pad/beneath the pad of Cu/low-K wafer during wirebonding process has been successfully predicted by finite element analysis (FEA). Tensile mechanical properties of ultra thin wire before/after electric flame-off (EFO) process have been investigated by self-design pull test fixture. The experimental obtained hardening value has significantly influence on localize stressed area on Al pad. This would result in Al pad squeezing around the smashed FAB during impact stage and the consequent thermosonic vibration stage. Microstructure of FAB and HAZ are also carefully measured by micro/nano indentation instruments. All the measured data serves as material inputs for the FEA explicit software ANSYS/LS-DYNA. Because the crack of low-k layer and delamination of copper via are observed, dynamic transient analysis is performed to inspect the overall stress/strain distributions on the microstructure of Cu/low-k wafer. Special emphasizes are focused on the copper via layout and optimal design of Cu/low-k microstructure. It is also shown that the Al pad can be replaced by Al-Cu alloy pad or Cu pad to avoid large deformation on pad and cracking beneath the surface. A series of comprehensive experimental works and FEA predictions have been performed to increase bondability and reliability in this study.",signatures:"Hsiang-Chen Hsu, Chin-Yuan Hu, Wei-Yao Chang, Chang-Lin Yeh and Yi-Shao Lai",downloadPdfUrl:"/chapter/pdf-download/12101",previewPdfUrl:"/chapter/pdf-preview/12101",authors:[null],corrections:null},{id:"12102",title:"Finite Element Analysis of Deformation and Fracture of Cylindrical Tubes under Internal Moving Pressures",doi:"10.5772/10222",slug:"finite-element-analysis-of-deformation-and-fracture-of-cylindrical-tubes-under-internal-moving-press",totalDownloads:4425,totalCrossrefCites:5,totalDimensionsCites:7,hasAltmetrics:0,abstract:null,signatures:"Majid Mirzaei",downloadPdfUrl:"/chapter/pdf-download/12102",previewPdfUrl:"/chapter/pdf-preview/12102",authors:[null],corrections:null},{id:"12112",title:"FE Analysis of Evolution of Defects during 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Agricultural ecosystems are highly dynamic and usually display apparent seasonal phenological patterns that are strongly dependent on local management practices. The timely and frequent determination of indicators of crop development and productivity, including phenological stage and biophysical parameters such as leaf (or plant) area index and above-ground biomass, is critical for supporting land management decision making in near-real-time. Synthetic aperture radars (SARs) are active systems that provide their own source of energy to illuminate ground targets in the microwave domain. Because the Earth’s atmosphere is largely transparent to microwaves, SAR sensors can be operated day or night and under almost at all weather conditions to acquire high-resolution earth observation data. Given that many regions of the world experience frequent cloud cover, SAR has become an essential remote sensing tool for the operational monitoring of agricultural production systems around the world.
Radar backscattering is highly sensitive to the structural (roughness, orientation, and spatial distribution of scattering components) and the dielectric properties of targets. The backscattering intensity is also strongly related to the transmitted microwave frequency, incident angle and the transmitted and received polarizations. Several microwave scattering models have been developed to relate backscattering to target properties and radar acquisition parameters. Examples of theoretical microwave models include the Integral Equation Model (IEM) and the MIMICS (Michigan Microwave Canopy Scattering) model [1, 2, 3]. Semi-empirical models maintain some theoretical basis but use empirical data to simplify the mathematical relationships between scattering and target properties as well as sensor parameters. Examples of this approach to modeling include the Water Cloud Model (WCM) used to characterize SAR response from vegetation and soil [4], as well as the Oh [5] and Dubois [6] models that relate soil properties to radar backscattering. Two simplified scenarios, based on which radar backscattering models have been developed, are shown in Figure 1. The first simplifies vegetation canopy as a layer of scattering elements uniformly distributed above the soil surface, and radar backscattering from the soil is modeled by a two-way attenuation through the canopy (left). The second takes into consideration of canopy geometric structure, and models three backscattering components, surface scattering from plant or soil, double-bounce scattering from plant and soil (plant-soil and soil-plant), and multiple scattering by the plant-soil mix (volume scattering) (right).
Simplified scenarios for modeling radar backscattering from vegetation canopy. Left: vegetation as a water cloud, and backscattering is modeled by a two-way attenuation through a canopy with a path length H/cos(ϴ). Right: vegetation as a 3-D scattering medium inducing three scattering mechanisms, surface scattering, double-bounce, and volume scattering.
Radar backscattering models have been used for estimation of crop parameters such as Leaf Area Index (LAI), canopy water content, and biomass [7, 8, 9, 10], and soil parameters such as soil moisture and surface roughness [11, 12], using SAR data acquired at different incident angles, frequencies, and/or polarizations. Fully polarimetric (or quad-pol) SAR systems measure the complete complex scattering from a target. Microwaves are transmitted and received in two orthogonal polarizations and the phase is preserved during processing. With the complete scattering matrix, quad-pol data can be analyzed to provide polarimetric features and the signal can be decomposed using coherent (e.g., Pauli and Cameron) or incoherent (e.g., Freeman-Durden and Cloude-Pottier) techniques [13, 14]. Variables derived through polarimetric decomposition can be used both in classification [15, 16] and parameter estimation, such as crop phenology or soil moisture [17, 18]. Time-series SAR data have also been used for the detection of crop seeding and harvest using change detection approaches [19, 20, 21].
A few satellite SAR constellations have been launched during the past few years, and more small satellite SAR constellations will be continuously developed in near future. The increasing availability of a large amount of SAR data, in companion with big data analytics, provides an unprecedented opportunity for effective and operational monitoring of agricultural ecosystems. However, the effective use of SAR data requires a full understanding of how the information it provides relates to agricultural targets. The objectives of this article are to summarize the path of SAR system development and to review the information sources of SAR data, the applications in agricultural ecosystem monitoring with a focus on crop classification, crop parameter estimation, and change detection using dense time-series data.
Early studies in radar remote sensing applications in agriculture relied extensively on ground-based microwave scatterometers [22, 23, 24, 25, 26, 27]. The portability of scatterometers allows them to be rapidly deployed to agricultural test sites to collect temporally dense data at different frequencies, polarizations, and incidence angles. Experiments using scatterometers have been critical for developing an understanding of how microwaves interact with soils and crops, and the development and testing of microwave models [28]. However, despite the important contributions of such research, scatterometer data are geographically limited to smaller test plots.
The deployment of SAR on aircraft and satellite platforms provides data at field and sub-field scales over much broader geographic extents. Airborne SAR campaigns, such as the NASA/JPL AIRSAR and UAVSAR, Canadian Convair-580 C/X SAR, and German DLR E-SAR/F-SAR, have served as theoretical testbeds to develop applications pre-launch of space-based SARs. Space-based SAR observations from the Shuttle Imaging Radar (SIR) missions, in particular the SIR-C/X SAR missions in 1994, provided imaging opportunities from a space platform and delivered data in different frequencies and polarizations.
Systematic acquisitions from SAR satellites began with the launch of ESA’s ERS-1 satellite in 1991. Several other space agencies followed, launching SAR satellites operating at different frequencies, and with different capacities to select imaging modes at a variety of spatial resolutions, swath widths, polarization, and incident angles (Table 1). RADARSAT-2, for example, supports the acquisition of data at single, dual, or quad polarization, at different spatial resolutions, and various incident angles. However, while the capability of each of these space-based systems was extensive, they demanded large and heavy payloads. For example, the mass of the Canadian C-band RADARSAT-1 and -2 satellites, and the ESA Sentinel-1A and 1B satellites each exceeded two tons at launch. More recently, technological developments that include standard electronic components and semiconductor materials (GaN) [29, 30] make it possible to produce compact SAR sensors in a shorter amount of time, and at a relatively low cost. These advancements have led to commercial investments in microsatellite constellations of space-borne SARs. For instance, PredaSAR plans to launch a constellation of 48 satellites equipped with a large swath C-band or a high-power X-band sensor. The Japanese QPS Institute is developing an X-band constellation that will eventually comprise 36 micro-satellites. These SAR sensors are typically small (<500 kg), but are more limited in the diversity of imaging modes, typically operating in only single or dual polarizations.
Platform | Country/organization | SAR system | Frequency | Mode | Active years | Note |
---|---|---|---|---|---|---|
Airborne | Canada | Convair-580 | X, C | Polarimetric | 1986–present | |
USA/NASA | AirSAR | C, L, P | Polarimetric | 1988–2004 | ||
USA/NASA | UAVSAR | Ka, L, P | Polarimetric | 2007–present | ||
German/DLR | E-SAR/F-SAR | X, C, S, L, P | Polarimetric | 1988–present | ||
USA.JPL | SIR-C/X | C, X | Polarimetric | 1994 | ||
Large satellites | ESA | ERS-1/2 | C | VV | 1991–2011 | |
ESA | ASAR | C | Various | 2002–2012 | ||
Japan/NASDA | JERS-1/2 | L | HH | 1992–1998 | ||
Canada | RADARSAT-1 | C | HH | 1995–2013 | ||
Canada | RADARSAT-2 | C | Various | 2007–present | ||
Canada | RCM | C | Various | 2019–present | 3 satellites | |
German | TerraSAR-X | X | Single or dual | 2007 | ||
Argentina | SAOCOM | L | Polarimetric | 2018–present | 2 satellites | |
ESA | Sentinel-1 | X | Single or dual | 2014–present | 4 | |
Italy | COSMO-SkyMed | X | Various | 2007–2010 | COSMO: 4 | |
2019–present | CSG: 2 | |||||
Japan/JAXA | ALOS-PALSAR | L | Various | 2006–present | 4 | |
USA/India | NISAR | L, S | Polarimetric | 2023–present | ||
Micro-satellites | Finland | ICEYE-X | X | VV | 2018–present | 18 |
Japan/Synspective | StriX | X | VV | 2020–present | 30 | |
Japan/QPS | QPS-SAR | X | Circular | 2019–present | 36 | |
USA | Capella | X | HH | 2018–present | 36 | |
USA | PredarSAR | C, X | — | 48 | ||
USA | Umbra-SAR | X | — | 2021–present | 12 |
List of SAR systems.
For reference, a non-exclusive list of SAR systems that are of interest to agricultural applications is given in Table 1. Over the past 15 years or so, the general trend of governments and space agencies has been to focus on larger wide-swath SARs whose data are free and open (or partially open) to the public. In comparison, the commercial SAR satellite ecosystems have focused on constellations of smaller satellites providing, for a fee, access to data at finer spatial and temporal resolutions. Data from such constellations may provide the near-continuous monitoring of land surfaces.
Crop growth dynamics are characterized by structural or moisture changes, which can be captured by time-series SAR data for the detection and mapping of phenological development stages [17, 31, 32, 33, 34]. Because different crops have different growth dynamics, time-series SAR is also useful for crop classification [35, 36, 37, 38]. Time-series optical and SAR data have been used to derive phenological metrics for phenology-based crop classification [35, 39, 40]. Using a dense stack of Sentinel-1 SAR data, Bargiel [35] proposed a crop classification scheme using phenological sequence patterns (PSP), which outperformed the Random Forest and the Maximum Likelihood classifiers for cereal crops. Phenology-based classifiers can be more generic than conventional classifiers, and may be more resilient to differences in management practices and growth conditions because they take crop-specific growth dynamics into consideration [35, 39].
Important target information is also revealed by different SAR polarizations. Polarizations that interact more strongly with plant volume will likely be more useful for crop parameter estimation and for discriminating different crops. For example, VV performs well in characterizing vertical vegetation structure, and VH is sensitive to multiple scattering events in the canopy [41], and thus their use in combination provides better classification capabilities in most cases. HH polarization is found to be inferior in many cases for these specific applications [28, 42]; however, it is more sensitive to the structural variation of rice, and thus useful for mapping this crop [43].
SAR backscatter intensity and other polarimetric parameters can be derived from fully polarimetric SAR using coherent or incoherent target decomposition methods, as summarized in Cloude and Pottier [13], Touzi et al. [14], and Lee and Pottier [44]. Simple or canonical targets—such as dipoles, diplanes, or cylinders—show higher coherence than distributed targets—such as rough soil surfaces or vegetation—where random scattering occurs. Criteria to determine coherent and incoherent targets are provided by Touzi and Charbonneau [45], using the maximum symmetric component derived from the Cameron decomposition. Coherent target decomposition is applied to express the complex scattering matrix as linear combinations of a set of simpler and independent bases, each representing certain physical scattering mechanisms. Examples include the Pauli decomposition, the Krogager decomposition, and the Cameron decomposition. Incoherent decomposition methods are used when a pixel contains distributed targets, and express the second-order statistics of coherency or covariance matrices with a combination of simpler components. Examples include the Freeman-Durden, Huynen, and Cloude-Pottier decompositions. For satellite SAR sensors, the power and pulse repetition required to operate a fully polarimetric system limits swath widths, and thus hybrid architectures, such as compact polarimetric systems, have been proposed [46]. Compact polarimetry offers a partial solution by transmitting a single circularly polarized wave and receiving two orthogonal waves coherently [46, 47, 48]. Methods for compact polarimetric data decomposition have also been developed and summarized in Charbonneau et al. [47], Cloude et al. [49], and Ponnurangam and Rao [50].
The distribution and orientation of plant components and their sizes relative to SAR wavelengths vary over the growing season and from crop to crop. Microwave scattering occurs when the SAR wavelength is similar to or smaller than the size of canopy components. SAR frequency also influences the penetration depth of microwave radiation into crop canopies. Lower frequencies (e.g., L-band) penetrate deeper into the canopy than higher frequencies (e.g., C-/X-band). The optimal depth of penetration, and the matching of wavelength to the size of plant components, vary from crop to crop and throughout the crop development cycle. As a result, the selection of a single best frequency for SAR is challenging. Higher frequency SAR is better for classifying low biomass canopies, while lower frequency SAR is more useful for identifying high biomass vegetation [42]. The integration of data at different frequencies brings enriched information for crop classification and has thus been widely recommended [42, 51, 52, 53, 54, 55, 56, 57]. However, implementing a multi-frequency approach is challenging due to limitations in the availability of data from sensors at different frequencies, especially for operational applications. Temporal signatures created by different frequencies have also been exploited for crop area mapping using dense time series of SAR data. Kraatz et al. [58] used the temporal coefficient of variation of the VH polarization from both Sentinel-1 C-Band and PALSAR L-band data, and an optimal threshold, to discriminate crops from non-crops in western Canada. A higher mapping accuracy was achieved using C-band data (84%) than L-band data (74%), though performance varied among different cover types. Here, L-band performed poorly for soybean and some non-crop types (urban, grassland, and pasture), while C-band was relatively poor for corn, urban, and pasture. A time series of data from both frequencies would likely have improved these accuracies.
The variation of radar backscattering with incident angle is another important consideration for mapping agricultural landscapes with SAR. This is reflected in vegetation backscattering models, such as the MIMICS model [3] and the Karam-Fung model [59]. These models, developed for forest and adapted for crops [43, 60], require incident angle as an explicit parameter. For example, Prevot et al. [61] showed that using a simple parametrization of the angular effect of soil roughness in the Water Cloud Model [4], the vegetation water content can be estimated satisfactorily from C- and X-band SAR data acquired at two different incident angles, for example, 20° and 40°. Various studies have demonstrated the impacts of incident angle on land cover classification. Poirier et al. [62] studied the impacts of incident angle on classification performance by acquiring C-band data near-coincident at two different angles (30° and 53°) with the Convair-580 airborne system. Results showed that SAR data collected at the larger incident angle interacted more with the upper canopy, delivering an improved classification. Kothapalli Venkata et al. [63] conducted a study to assess the separation of corn from other land cover types (wheat, fallow, water, and urban) using multi-incident angles (28°, 42°, and 52°) C-band hybrid polarimetric data acquired over 3 days by RISAT. The study showed that corn can be discriminated from other crop types using volume and double-bounce scattering at both 28° and 42°, and using odd bounce and volume scattering combinations at 52°. Xu et al. [64] acquired RADARSAT-2 data at three different incident angles and showed that multi-angle SAR improved the classification accuracy of some land cover types (though it should be noted that the images were acquired at different times during 1 month, confounding the effects of the time of acquisition and change in incident angle). In summary, SAR data acquired at different incident angles contribute to target information extraction.
The classification of land covers and crop types is one of the earliest applications of SAR in agriculture. In the broadest sense, crop classification from SAR involves the implementation of automated techniques to sort image data into one of a finite number of crop classes based on their backscatter characteristics. Crop classification is an important agricultural application because it can be used to derive the area seeded to individual crops, and to predict or forecast food production if crop growth conditions are incorporated. Obtaining this information requires detailed, routine, and frequent mapping of croplands with sufficiently high accuracy. SAR has been shown to be particularly useful for the operational monitoring of crop dynamics in agricultural ecosystems.
A broad array of approaches for classifying satellite images have been developed in the past few decades. Until recently, the Maximum Likelihood (ML) classifier was the most widely used method for the supervised classification of remote sensing data [65, 66, 67], mostly due to its simplicity in implementation. While this approach has been widely applied in different studies for satellite image classification of agricultural regions [68, 69, 70, 71], limitations associated with the ML approach mean that alternative supervised classification techniques are more preferable. Of these new methods, artificial neural networks (ANN) [72, 73, 74, 75], support vector machines (SVM) [76, 77, 78, 79], Decision Trees (DT) and ensembles of classification trees such as Random Forest (RF) [80, 81, 82, 83, 84] have all shown great promise.
A detailed comparison of classification methods is beyond the scope of this article, and indeed, would only provide limited insight into the best classification approaches for SAR-based crop type mapping. This is because the success of crop classification procedures is as much—if not more—dependent on the quality of the ground (in situ) observations used for training and validating the classification, than the actual algorithm chosen to do the classification. Instead, we direct readers to a comprehensive synthesis of this body of work provided by Khatami et al. [85], who conducted a statistical meta-analysis of research on land-cover classification. This study was conducted to provide coherent guidance on the relative performance of different classification processes for generating land cover products and showed that the highest mapping accuracies were provided by implementations of SVMs, ANNs, and RF. While it is important to note that these results are not necessarily predictive of the relative performance of any specific classifier in any specific application (due to the unique features of that application), they do provide an insight into how each classification algorithm may perform under various circumstances [85].
In addition to the general classifiers presented above, two other classification schemes have been developed specifically for SAR data. These are classifications based on the Cloude-Pottier decomposition and classification based on the complex Wishart distribution. The Cloude-Pottier decomposition [15] produces three parameters—entropy (H), anisotropy (A), and the alpha angle (α). Entropy is a metric of the degree of randomness of scattering from within the resolution cell, anisotropy is an indicator of the presence of secondary or tertiary scattering mechanisms, and the alpha angle represents the dominant scattering mechanism. A classification scheme was developed to divide the H-α space into eight possible scattering zones, from which land cover classifications can be performed. The advantage of this classification scheme is the improved understanding of SAR signal scattering mechanisms where there is less
SAR data are typically multi-look processed for speckle noise reduction. The covariance matrix of the multi-look processed SAR data follows a multivariate complex Wishart distribution. With this condition, Lee et al. [91] proposed a classification scheme using Bayes maximum likelihood or minimum distance (MD) classifier. In practice, each class is characterized by an elementary covariance matrix derived from training samples, and each pixel is classified according to the Bayes likelihood with the elementary covariance matrices under a given
The coordination of Earth Observations (EO) data for agricultural monitoring necessitates the articulation of spatially explicit EO data requirements, including where [92], when [93], how frequently [94], over which spectral range, and at what spatial resolution these data are needed [95]. Because cropping systems are often diverse and complex, and the types of crops grown and the timing of their growth vary from region to region, the best choice of sensors to be used, the optimal number of images required, and the timing of image acquisitions are usually geographically specific. Where SAR has been used in operational national-scale crop mapping programs, it has usually been integrated with optical remote sensing data. Both optical and SAR provide unique and valuable information relating to plant growth and type, primarily due to their different wavelengths. Optical imagery acquired in the near-infrared and shortwave-infrared is sensitive to canopy biochemistry such as composition and concentration of pigments, water content, biomass, and leaf internal structure, while SAR imagery is sensitive to plant structure. SAR observations are also critical for filling gaps in the optical image record brought about by the presence of clouds during key growth stages.
The integration of optical and SAR data can be as simple as combining data from different sources into raster stacks for classification, sometimes applying mathematical transformations to fuse and enhance features or reduce data dimensionality [96, 97, 98, 99]. In some cases, SAR data are not used directly in the classification process but are first transformed into higher-level data products. This has included the derivation of phenological metrics from SAR time series (e.g., Torbick et al. [100] and the use of SAR-based texture [101].
One of the most well-known applications of SAR in national-scale crop mapping comes from Canada. Since 2010, Agriculture and Agri-Food Canada—Canada’s Ministry of Agriculture—has integrated C-band SAR (RADARSAT, Sentinel-1) with optical data streams (Landsat-5, -7 and -8, SPOT, DMC, RapidEye, and Resourcesat-1) to generate its Annual Space-Based Crop Inventory for Canada [102]. Figure 2 shows the mapping result for 2020, which covers the agricultural land and includes all crops and a few other land cover types.
National scale crop type mapping in Canada, 2020. The map is produced by Agriculture, Geomatics and Earth Observation Division, Science & Technology Branch, Agriculture, and Agri-Food Canada.
Results from research and operations suggest that optical and SAR satellite data are both required to best characterize the key crop growing (phenological) stages required for high-accuracy crop mapping at a national scale [39, 56, 80, 103, 104, 105, 106]. The addition of dual-pol SAR has been shown to increase accuracies over the use of optical data alone by as much as 16% [42, 104]. Nonetheless, the decision to use optical and/or SAR is usually determined by the trade-off among a number of factors, including—(a) the heterogeneous and dynamic intrinsic nature of the agro-ecosystem being studied; (b) the geographical extent to be mapped; (c) the minimum mapping unit required to resolve individual fields and other meaningful ecological units (e.g., wetlands, woodlots, etc.); (d) differences in crop cycles; (e) differences in cropping practices and calendars within the same class; (f) the spectral similarity with other land cover classes; (g) the engineering constraints of the remote sensing systems (i.e., swath size; spatial, temporal, spectral and radiometric resolutions; cloud coverage for optical systems), and (h) data availability (i.e., open, fee-based).
Microwave scattering, represented by both intensity and phase characteristics, changes with variations in the structure of crop canopies and canopy water content. Canopy structure and water content vary as crops develop and are thus indicative of crop development and productivity. Figure 3 shows seasonal variation of radar backscattering intensity of annual crops over a growing season in an agricultural region in northern Ontario, Canada, using dual-polarization C-band SAR data acquired by Sentinel-1 in 2019. Both VV and VH polarizations of annual crops show obvious seasonal variation patterns characteristic to crop development cycle, whereas that of forest targets (the two dotted lines) remain at a relatively stable and higher level throughout the season. This clearly shows a positive correlation between radar backscattering intensity and crop live biomass, based on which different crop parameters can be estimated from SAR data.
Seasonal profiles of radar backscattering intensity for annual crops in northern Ontario, Canada, using C-band SAR data acquired by Sentinel-1 in 2019. The two dotted lines represent two forest patches.
The potential of SAR for supporting crop growth monitoring through the quantitative estimation of crop parameters—such as Leaf (or Plant) Area Index (LAI or PAI), plant height and density, fresh and dry biomass, and plant water content—depends on SAR sensor characteristics (frequency, polarization, and incident angle), crop type, and growth stage [107, 108].
The characteristics of a SAR determine the depth to which a pulse of microwave energy can penetrate a plant canopy and, in turn, influence the ability to determine canopy conditions from SAR observations. Because of this, the optimal choice of SAR frequency will vary over time, depending on canopy type and growth stage, and thus the use of multiple SAR frequencies for crop mapping, where available, is recommended. SAR scattering is also polarization-dependent [44, 109, 110]. Overall, the best polarization for crop characterization has been the linear cross polarization (either HV or VH) [110, 111]. This is mainly due to re-polarization that occurs during multiple scattering within targets with complex structures, such as crop canopies consisting of randomly oriented and distributed stems and leaves [112]. Using RADARSAT-2 SAR data, Liao et al. [113] studied the sensitivity of C-band SAR polarimetric parameters for the estimation of crop height and fractional vegetation cover. They found that cross polarization or combinations of dual polarizations (HH-VV or HV-VV) were strongly correlated with crop height and fractional cover of broadleaf crops, such as corn, with degraded performance toward the later growing stages. For narrow-leaf crops, such as wheat, the sensitivity of SAR parameters to crop height and cover fraction was relatively low or inconsistent. Wali et al. [114] assessed Sentinel-1 C-band SAR VV and VH backscatter for estimating biophysical parameters of rice, including plant height, green vegetation cover, LAI, and total dry biomass. The results of this study showed that both VH and VV were strongly and linearly correlated with biophysical parameters until backscatter saturated during the mid-reproductive stage (60 days after transplanting), and the beginning of the reproductive stage for VV (though VH showed stronger correlations in most cases). Chauhan et al. [115] were able to obtain better estimates of vegetation parameters by accounting for soil backscatter effects. Other studies include those by Xie et al. [110], who demonstrated the capability of RADARSAT-2 polarimetric SAR variables for crop height estimation, and Hosseini et al. [116], who used WCM and SVM to estimate LAI using RADARSAT-2 SAR intensity collected over multiple international sites (Argentina, Canada, Germany, India, Poland, Ukraine, and the U.S).
Polarimetric SAR allows the complete scattering characteristics of crop canopies to be determined, and parameters derived from these complex data can improve estimates of crop conditions. Many recent examples of this come from studies over agricultural regions in Canada. Using C-band RADARSAT-2 polarimetric data, Wiseman et al. [117] extracted and evaluated 21 polarimetric parameters to estimate dry biomass for canola, corn, soybean, and spring wheat crops. This study found that most SAR parameters were significantly correlated with dry biomass accumulation, while several proved to be good indicators of changes in crop structure and phenology. For instance, four SAR responses (linear HV and circular LR backscatter, volume scattering, and pedestal height) increased during canola ripening. However, as canola flowered, the importance of these parameters declined. Homayouni et al. [118] used the ratio of volume-to-surface scattering derived from C-band RADARSAT-2 polarimetric data to monitor the growth of canola, corn, spring wheat, and soybeans fields in western Canada. They found that this ratio was strongly correlated with optical vegetation indices (e.g., the normalized difference vegetation index NDVI, and the Soil Adjusted Vegetation Index SAVI). Using time-series RADARSAT-2 polarimetric data, and RapidEye optical imagery, Jiao et al. [119] applied a semi-empirical Canopy Structure Dynamics Model, Growing Degree Days, and SAR parameters calibrated to optical NDVI to derive daily estimates of canola crop condition over an entire growing season. Correlations (R values) of 0.63–0.84 were reported when SAR parameters were related to optical NDVI, with results varying among three growing seasons.
A growing literature focusing on SAR-based vegetation indices demonstrates the potential of such techniques. Kim and van Zyl [120] proposed a radar vegetation index (RVI) based on the SAR backscatter intensities at VV, HH, and VH polarizations, which has since been simplified to accommodate data obtained from dual-polarized systems [121, 122]. Using Sentinel-1 observations, Periasamy [123] proposed a Dual Polarization SAR Vegetation index (DPSVI) by exploiting the data distribution of VV and VH backscatter coefficients in two-dimensional space. Such radar indices show strong potential for the better discrimination of bare soil from vegetation, as well as for crop structural parameter estimation. Other SAR-based vegetation indices include the SAR simple difference (SSD) index, applied to estimate rice yield in China, and based on the difference in Sentinel-1 VH backscatter between the end of the rice tillering stage and the end of grain filling stage [124]. Results of the study showed a strong exponential relationship between the SSDVH and rice yield.
Other applications of SAR for crop parameter estimation and growth condition monitoring include its use in radiative models. Attema and Ulaby [4] developed a Water Cloud Model (WCM) to simulate SAR backscatter from the crop-soil system as an incoherent sum of contributions from plants and background soil after a two-way attenuation by canopies. Through time, the model has been modified to reflect different approaches to the interaction and parameterization of soil and vegetation contributions. For example, various studies have used LAI, canopy water content, and biomass to characterize the vegetation component in the WCM [7, 8, 10, 116].
In the context of this article, the objective of change detection using remote sensing data is to identify and characterize changes in agricultural land cover and/or use (e.g., conversions from one crop class to another) or changes in condition within a land cover and/or use (e.g., modifications within a crop class) over a specified period of time. These changes can be described as—(a) binary change/non-change (e.g., harvest); (b) from-to trajectories (e.g., forest to cropland conversion); (c) causes of change (e.g., fire, flooding); and (d) continuous variable change (e.g., reduced productivity within a class due to insect infestation or drought) [125]. Understanding the types of change sought is critical for selecting suitable remote sensing data sources, determining processing methods, and developing and implementing robust and effective change detection algorithms.
For agricultural resource management, it is important to detect intra-annual landscape changes, such as changes in crop phenology [17, 20, 33], field operations [19, 21], and field conditions [126, 127]. This type of monitoring requires dense remote sensing time series that usually cannot be fulfilled using optical data alone due to the presence of the cloud. As a result, spatially and temporally comprehensive and consistent coverages from operational wide-swath SAR satellites will continue to be a critical source of free and open SAR data for national-scale change detection. As new SAR missions are launched and existing missions expand, multi-frequency SAR is expected to play an increasingly important role in monitoring and measuring change on agricultural landscapes. The application of SAR for within-season change detection will require well-calibrated data from multiple satellites within a constellation, if satellites from different constellations are used together.
Change detection using SAR backscatter—as opposed to its indirect detection from SAR-derived value-added products such as crop type maps or modeled biophysical parameters—belong to one of two broad types. These are Incoherent Change Detection (ICD) and Coherent Change Detection (CCD) [128]. ICD methods identify changes in mean backscatter intensity without considering SAR phase information. Here, the difference can be calculated as a ratio, a log ratio (LR), a mean ratio (MR), the normalized compression distance [129], or using pointwise approaches based on graph theory [130], convolutional neural networks [131], or the generalized likelihood ratio test (GLRT) [132]. In comparison, CCD methods identify change based on the complex conjugate correlation coefficient of the two images, thus taking into consideration of both backscatter intensity and phase. If dense stacks of time-series SAR images are available, changes can be inferred from these methods. For example, Shang et al. [21] used CCD to detect crop seeding and harvest using time-series Sentinel-1 SAR. The study integrated time-series coherence and VH backscatter intensity to detect changes at the beginning and at the end of a growing season, with the assumption that coherence is comparatively higher before crops emerge and after crop harvest. Figure 4 shows the example for mapping crop seeding dates, and details of the approach are given in Shang et al. [21].
Estimation of crop seeding dates through change detection using C-band SAR data acquired by Sentinel-1. Left: detection of annual crop fields using a simple threshold of seasonal variation amplitude of VH; right: mapping of crop seeding dates.
Timely and continuous observations from satellite systems are critical for providing the data and information required by decision-makers to manage agricultural lands. High-quality satellite observations can be obtained from SAR sensors; however, they must be collected at a spatial resolution that allows sufficient detail to be resolved, and at times, during the growing season, that coincides with the key growth stages of crops being assessed. The most accurate detailed national crop mapping generally occurs when moderate-resolution spectrally rich time series are acquired that contain no gaps.
Because of its near all-weather capacity, SAR technology has been shown to be particularly useful in agricultural monitoring, especially in regions with frequent cloud cover. The agricultural applications summarized in this article cover examples of information extraction for crops. Despite the gains made over the past 15 years in methods for crop monitoring from SAR, some challenges remain. A major challenge is the separation of backscattering signals from soils and crops, where it is difficult to differentiate the geometrical and dielectric properties of these two targets. While theoretical and semi-empirical models have been developed to simulate backscattering signals, model inversion for solving surface parameters with high accuracy remains a challenge. Much attention has been focused on the integration of SAR and optical remote sensing for improving target parameter retrieval accuracies. With temporally dense imaging capabilities of current and future satellite SAR systems, changes in agricultural land should be more accurately detected. Methods for change detection based on SAR and optical time series show large future potential.
Future opportunities for the use of SAR in agricultural monitoring will come from the adoption of new and improved satellite missions that, in combination or isolation, will allow a better characterization of crop-specific growth cycles at the field level. Of particular interest is the integration of SAR imagery acquired at multiple frequencies, especially if these multi-frequency data sets are collected in wide swaths, with consistent coverages, and under open data policies. However, this will not be without a challenge. The ability of national mapping agencies to incorporate this information in a timely and efficient manner will require significant investment in information technology infrastructure to facilitate the processing of significantly greater volumes of data.
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\\n\\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\\n\\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nOAI-PMH
\\n\\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\\n\\nLicense
\\n\\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\\n\\nPeer Review Policies
\\n\\nAll scientific works are Peer Reviewed prior to publishing. Read more
\\n\\nOA Publishing Fees
\\n\\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\\n\\nDigital Archiving Policy
\\n\\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\\n\\nOpen Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
\\n\\nOpen Science is about increased rigour, accountability, and reproducibility for research. It is based on the principles of inclusion, fairness, equity, and sharing, and ultimately seeks to change the way research is done, who is involved and how it is valued. It aims to make research more open to participation, review/refutation, improvement and (re)use for the world to benefit.
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The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\n\nOpen Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
\n\nOpen Science is about increased rigour, accountability, and reproducibility for research. It is based on the principles of inclusion, fairness, equity, and sharing, and ultimately seeks to change the way research is done, who is involved and how it is valued. It aims to make research more open to participation, review/refutation, improvement and (re)use for the world to benefit.
\n\nOpen Science refers to doing traditional science with more transparency involved at various stages, for example by openly sharing code and data. It implies a growing set of practices - within different disciplines - aiming at:
\n\nWe aim at improving the quality and availability of scholarly communication by promoting and practicing:
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. He has contributed in stochastic estimation of control area especially, in the Multiple Target Tracking and Interactive Multiple Model (IMM) research, Ball & Beam Control Problem, Robotics, Levitation Control. He has contributed in developing Algorithms for Fingerprint Matching, Computer Vision and Face Recognition. He has been supervising Pattern Recognition, Formal Languages and Distributed Processing projects for several years. He has reviewed many books on Management, Computer Science. Currently, he is an active and permanent reviewer for many international conferences and symposia and the program committee member for many international conferences.\nIn teaching he has taught the core computer science subjects like, Digital Design, Real Time Embedded System Programming, Operating Systems, Software Engineering, Data Structures, Databases, Compiler Construction. In the Engineering side, Digital Signal Processing, Computer Architecture, Electronics Devices, Digital Filtering and Engineering Management.\nApart from his Academic Interest and activities he loves sport especially, Cricket, Football, Snooker and Squash. He plays cricket for Esbjerg city in the second division team as an opener wicket keeper batsman. 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Some physical treatments (UV, cold or heat,..) also provide germination improvement thus suggesting that priming effects are not necessarily related to seed imbibition. A better understanding of the metabolic events taking place during the priming treatment and the subsequent germination should help to use this simple and cheap technology in a more efficient way.',book:{id:"5218",slug:"new-challenges-in-seed-biology-basic-and-translational-research-driving-seed-technology",title:"New Challenges in Seed Biology",fullTitle:"New Challenges in Seed Biology - Basic and Translational Research Driving Seed Technology"},signatures:"Stanley Lutts, Paolo Benincasa, Lukasz Wojtyla, Szymon Kubala S,\nRoberta Pace, Katzarina Lechowska, Muriel Quinet and Malgorzata\nGarnczarska",authors:[{id:"94090",title:"Prof.",name:"Stanley",middleName:null,surname:"Lutts",slug:"stanley-lutts",fullName:"Stanley Lutts"},{id:"181730",title:"Prof.",name:"Paolo",middleName:null,surname:"Benincasa",slug:"paolo-benincasa",fullName:"Paolo Benincasa"},{id:"181732",title:"Dr.",name:"Lukasz",middleName:null,surname:"Wojtyla",slug:"lukasz-wojtyla",fullName:"Lukasz Wojtyla"},{id:"181733",title:"Dr.",name:"Szymon",middleName:null,surname:"Kubala",slug:"szymon-kubala",fullName:"Szymon Kubala"},{id:"181734",title:"Mrs.",name:"Katzzarina",middleName:null,surname:"Lechowska",slug:"katzzarina-lechowska",fullName:"Katzzarina Lechowska"},{id:"181735",title:"Dr.",name:"Muriel",middleName:null,surname:"Quinet",slug:"muriel-quinet",fullName:"Muriel Quinet"},{id:"181736",title:"Prof.",name:"Malgorzata",middleName:null,surname:"Garnczarska",slug:"malgorzata-garnczarska",fullName:"Malgorzata Garnczarska"}]},{id:"44143",doi:"10.5772/54592",title:"Production of Anthocyanins in Grape Cell Cultures: A Potential Source of Raw Material for Pharmaceutical, Food, and Cosmetic Industries",slug:"production-of-anthocyanins-in-grape-cell-cultures-a-potential-source-of-raw-material-for-pharmaceuti",totalDownloads:7939,totalCrossrefCites:24,totalDimensionsCites:70,abstract:null,book:{id:"3143",slug:"the-mediterranean-genetic-code-grapevine-and-olive",title:"The Mediterranean Genetic Code",fullTitle:"The Mediterranean Genetic Code - Grapevine and Olive"},signatures:"Anthony Ananga, Vasil Georgiev, Joel Ochieng, Bobby Phills and Violeta Tsolova",authors:[{id:"74792",title:"Dr.",name:"Joel W.",middleName:null,surname:"Ochieng",slug:"joel-w.-ochieng",fullName:"Joel W. Ochieng"},{id:"126149",title:"Dr.",name:"Anthony",middleName:null,surname:"Ananga",slug:"anthony-ananga",fullName:"Anthony Ananga"},{id:"136830",title:"Dr.",name:"Devaiah",middleName:null,surname:"Kambiranda",slug:"devaiah-kambiranda",fullName:"Devaiah Kambiranda"},{id:"137412",title:"Dr.",name:"Violetka",middleName:null,surname:"Tsolova",slug:"violetka-tsolova",fullName:"Violetka Tsolova"},{id:"165414",title:"Dr.",name:"Vasil",middleName:null,surname:"Georgiev",slug:"vasil-georgiev",fullName:"Vasil Georgiev"},{id:"165415",title:"Dr.",name:"Bobby",middleName:null,surname:"Phills",slug:"bobby-phills",fullName:"Bobby Phills"}]},{id:"43341",doi:"10.5772/54859",title:"Abiotic Stress in Plants and Metabolic Responses",slug:"abiotic-stress-in-plants-and-metabolic-responses",totalDownloads:5308,totalCrossrefCites:25,totalDimensionsCites:60,abstract:null,book:{id:"3226",slug:"abiotic-stress-plant-responses-and-applications-in-agriculture",title:"Abiotic Stress",fullTitle:"Abiotic Stress - Plant Responses and Applications in Agriculture"},signatures:"Saúl Fraire-Velázquez and Victor Emmanuel Balderas-Hernández",authors:[{id:"51144",title:"Dr.",name:"Saul",middleName:null,surname:"Fraire",slug:"saul-fraire",fullName:"Saul Fraire"},{id:"156646",title:"Dr.",name:"Victor Emmanuel",middleName:null,surname:"Balderas-Hernández",slug:"victor-emmanuel-balderas-hernandez",fullName:"Victor Emmanuel Balderas-Hernández"}]},{id:"43220",doi:"10.5772/52779",title:"Abiotic Stress Responses in Plants: Unraveling the Complexity of Genes and Networks to Survive",slug:"abiotic-stress-responses-in-plants-unraveling-the-complexity-of-genes-and-networks-to-survive",totalDownloads:7347,totalCrossrefCites:8,totalDimensionsCites:60,abstract:null,book:{id:"3226",slug:"abiotic-stress-plant-responses-and-applications-in-agriculture",title:"Abiotic Stress",fullTitle:"Abiotic Stress - Plant Responses and Applications in Agriculture"},signatures:"Ana Sofia Duque, André Martinho de Almeida, Anabela Bernardes da Silva, Jorge Marques da Silva, Ana Paula Farinha, Dulce Santos, Pedro Fevereiro and Susana de Sousa Araújo",authors:[{id:"59945",title:"Dr.",name:"Dulce",middleName:null,surname:"Santos",slug:"dulce-santos",fullName:"Dulce Santos"},{id:"59946",title:"Prof.",name:"Pedro",middleName:null,surname:"Fevereiro",slug:"pedro-fevereiro",fullName:"Pedro Fevereiro"},{id:"156799",title:"Dr.",name:"Susana",middleName:null,surname:"Araújo",slug:"susana-araujo",fullName:"Susana Araújo"},{id:"165802",title:"Dr.",name:"Ana Sofia",middleName:null,surname:"Duque",slug:"ana-sofia-duque",fullName:"Ana Sofia Duque"},{id:"165803",title:"Dr.",name:"André",middleName:null,surname:"Almeida",slug:"andre-almeida",fullName:"André Almeida"},{id:"165804",title:"Prof.",name:"Anabela",middleName:null,surname:"Silva",slug:"anabela-silva",fullName:"Anabela Silva"},{id:"165805",title:"Prof.",name:"Jorge",middleName:null,surname:"Marques Da Silva",slug:"jorge-marques-da-silva",fullName:"Jorge Marques Da Silva"},{id:"165806",title:"Dr.",name:"Ana Paula",middleName:null,surname:"Farinha",slug:"ana-paula-farinha",fullName:"Ana Paula Farinha"}]}],mostDownloadedChaptersLast30Days:[{id:"50720",title:"Use of Organic Fertilizers to Enhance Soil Fertility, Plant Growth, and Yield in a Tropical Environment",slug:"use-of-organic-fertilizers-to-enhance-soil-fertility-plant-growth-and-yield-in-a-tropical-environmen",totalDownloads:4940,totalCrossrefCites:10,totalDimensionsCites:16,abstract:"Soils rarely have sufficient nutrient for crops to reach their potential yield. Applying organic fertilizers without prior knowledge of their properties may cause yield decline under low application or pollute the environment with excessive application. Understanding the nutrient variability and release pattern of organic fertilizers is crucial to supply plants with sufficient nutrients to achieve optimum productivity, while also rebuilding soil fertility and ensuring protection of environmental and natural resources. This chapter presents the authors’ experiences with different organic amendments under Hawaii's tropical conditions, rather than an intensive literature review. For meat and bone meal by‐products (tankage), batch‐to‐batch variability, nutrient content/release pattern and quality, and plant growth response to the liquid fertilizer produced from tankage were evaluated. For animal livestock, dairy manure (DM) and chicken manure (CM) quality, changes in soil properties, and crop biomass production and root distributions were evaluated. For seaweed, an established bio‐security protocol, nutrient, especially potassium (K) variability, and plant growth and yield response were evaluated in different tropical soils.",book:{id:"5179",slug:"organic-fertilizers-from-basic-concepts-to-applied-outcomes",title:"Organic Fertilizers",fullTitle:"Organic Fertilizers - From Basic Concepts to Applied Outcomes"},signatures:"Amjad A. Ahmad, Theodore J.K. Radovich, Hue V. Nguyen, Jensen\nUyeda, Alton Arakaki, Jeana Cadby, Robert Paull, Jari Sugano and\nGlenn Teves",authors:[{id:"178933",title:"Dr.",name:"Amjad",middleName:"A.",surname:"Ahmad",slug:"amjad-ahmad",fullName:"Amjad Ahmad"},{id:"184973",title:"Dr.",name:"Theodore",middleName:null,surname:"Radovich",slug:"theodore-radovich",fullName:"Theodore Radovich"},{id:"184974",title:"Prof.",name:"Hue",middleName:null,surname:"Nguyen",slug:"hue-nguyen",fullName:"Hue Nguyen"},{id:"184975",title:"MSc.",name:"Jensen",middleName:null,surname:"Uyeda",slug:"jensen-uyeda",fullName:"Jensen Uyeda"},{id:"184976",title:"MSc.",name:"Alton",middleName:null,surname:"Arakaki",slug:"alton-arakaki",fullName:"Alton Arakaki"},{id:"184977",title:"Mr.",name:"Glenn",middleName:null,surname:"Teves",slug:"glenn-teves",fullName:"Glenn Teves"},{id:"184978",title:"MSc.",name:"Jeana",middleName:null,surname:"Cadby",slug:"jeana-cadby",fullName:"Jeana Cadby"},{id:"184979",title:"Prof.",name:"Robert",middleName:null,surname:"Paull",slug:"robert-paull",fullName:"Robert Paull"},{id:"184980",title:"MSc.",name:"Jari",middleName:null,surname:"Sugano",slug:"jari-sugano",fullName:"Jari Sugano"}]},{id:"59450",title:"Health Benefits of Fruits and Vegetables: Review from Sub-Saharan Africa",slug:"health-benefits-of-fruits-and-vegetables-review-from-sub-saharan-africa",totalDownloads:3309,totalCrossrefCites:3,totalDimensionsCites:6,abstract:"A fruit is defined as the edible part of a plant that consists of the seeds and surrounding tissues, while vegetables are plants cultivated for their edible parts. Fruits and vegetables are important sources of micronutrients and dietary fibres and are components of a healthy diet, which help in preventing major diseases. Due to the fact that fruits and vegetables have health promoting properties, they contribute to dietary guidance. This chapter defines the basic concepts related to health benefits of fruits and vegetables, reviews the previous literature on health benefits of fruits and vegetables and enumerates the health benefits of some common fruits and vegetables. It also examined the dietary recommendation of fruits and vegetables in less developed countries as well as present situation of fruits and vegetables consumption with particular reference to sub-Saharan Africa.",book:{id:"6492",slug:"vegetables-importance-of-quality-vegetables-to-human-health",title:"Vegetables",fullTitle:"Vegetables - Importance of Quality Vegetables to Human Health"},signatures:"Ifeoluwapo Amao",authors:[{id:"223341",title:"Dr.",name:"Ifeoluwapo",middleName:null,surname:"Amao",slug:"ifeoluwapo-amao",fullName:"Ifeoluwapo Amao"}]},{id:"51934",title:"Seed Priming: New Comprehensive Approaches for an Old Empirical Technique",slug:"seed-priming-new-comprehensive-approaches-for-an-old-empirical-technique",totalDownloads:7908,totalCrossrefCites:50,totalDimensionsCites:109,abstract:'Seed priming is a pre-sowing treatment which leads to a physiological state that enables seed to germinate more efficiently. The majority of seed treatments are based on seed imbibition allowing the seeds to go through the first reversible stage of germination but do not allow radical protrusion through the seed coat. Seeds keeping their desiccation tolerance are then dehydrated and can be stored until final sowing. During subsequent germination, primed seeds exhibit a faster and more synchronized germination and young seedlings are often more vigorous and resistant to abiotic stresses than seedlings obtained from unprimed seeds. Priming often involves soaking seed in predetermined amounts of water or limitation of the imbibition time. The imbibition rate could be somehow controlled by osmotic agents such as PEG and referred as osmopriming. Halopriming implies the use of specific salts while "hormopriming" relies on the use of plant growth regulators. Some physical treatments (UV, cold or heat,..) also provide germination improvement thus suggesting that priming effects are not necessarily related to seed imbibition. A better understanding of the metabolic events taking place during the priming treatment and the subsequent germination should help to use this simple and cheap technology in a more efficient way.',book:{id:"5218",slug:"new-challenges-in-seed-biology-basic-and-translational-research-driving-seed-technology",title:"New Challenges in Seed Biology",fullTitle:"New Challenges in Seed Biology - Basic and Translational Research Driving Seed Technology"},signatures:"Stanley Lutts, Paolo Benincasa, Lukasz Wojtyla, Szymon Kubala S,\nRoberta Pace, Katzarina Lechowska, Muriel Quinet and Malgorzata\nGarnczarska",authors:[{id:"94090",title:"Prof.",name:"Stanley",middleName:null,surname:"Lutts",slug:"stanley-lutts",fullName:"Stanley Lutts"},{id:"181730",title:"Prof.",name:"Paolo",middleName:null,surname:"Benincasa",slug:"paolo-benincasa",fullName:"Paolo Benincasa"},{id:"181732",title:"Dr.",name:"Lukasz",middleName:null,surname:"Wojtyla",slug:"lukasz-wojtyla",fullName:"Lukasz Wojtyla"},{id:"181733",title:"Dr.",name:"Szymon",middleName:null,surname:"Kubala",slug:"szymon-kubala",fullName:"Szymon Kubala"},{id:"181734",title:"Mrs.",name:"Katzzarina",middleName:null,surname:"Lechowska",slug:"katzzarina-lechowska",fullName:"Katzzarina Lechowska"},{id:"181735",title:"Dr.",name:"Muriel",middleName:null,surname:"Quinet",slug:"muriel-quinet",fullName:"Muriel Quinet"},{id:"181736",title:"Prof.",name:"Malgorzata",middleName:null,surname:"Garnczarska",slug:"malgorzata-garnczarska",fullName:"Malgorzata Garnczarska"}]},{id:"61691",title:"Role of Vegetables in Human Nutrition and Disease Prevention",slug:"role-of-vegetables-in-human-nutrition-and-disease-prevention",totalDownloads:3197,totalCrossrefCites:12,totalDimensionsCites:22,abstract:"Vegetables are important for human health because of their vitamins, minerals, phytochemical compounds, and dietary fiber content. Especially antioxidant vitamins (vitamin A, vitamin C, and vitamin E) and dietary fiber content have important roles in human health. Adequate vegetable consumption can be protective some chronic diseases such as diabetes, cancer, obesity, metabolic syndrome, cardiovascular diseases, as well as improve risk factors related with these diseases. In this chapter, basic information will be given about the classification of vegetables, preparation and cooking, and their effects on food content of vegetables and effects on health and diseases (diabetes, obesity, metabolic syndrome, cardiovascular diseases, and cancer).",book:{id:"6492",slug:"vegetables-importance-of-quality-vegetables-to-human-health",title:"Vegetables",fullTitle:"Vegetables - Importance of Quality Vegetables to Human Health"},signatures:"Taha Gökmen Ülger, Ayşe Nur Songur, Onur Çırak and Funda Pınar\nÇakıroğlu",authors:[{id:"176588",title:"Prof.",name:"Funda Pınar",middleName:null,surname:"Çakıroğlu",slug:"funda-pinar-cakiroglu",fullName:"Funda Pınar Çakıroğlu"},{id:"244239",title:"Dr.",name:"Onur",middleName:null,surname:"Çırak",slug:"onur-cirak",fullName:"Onur Çırak"},{id:"251662",title:"Dr.",name:"Ayşe Nur",middleName:null,surname:"Songür",slug:"ayse-nur-songur",fullName:"Ayşe Nur Songür"},{id:"251663",title:"MSc.",name:"Taha Gökmen",middleName:null,surname:"Ülger",slug:"taha-gokmen-ulger",fullName:"Taha Gökmen Ülger"}]},{id:"62292",title:"Introductory Chapter: Quality Vegetable Production and Human Health Benefits",slug:"introductory-chapter-quality-vegetable-production-and-human-health-benefits",totalDownloads:1724,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"6492",slug:"vegetables-importance-of-quality-vegetables-to-human-health",title:"Vegetables",fullTitle:"Vegetables - Importance of Quality Vegetables to Human Health"},signatures:"Md Asaduzzaman and Toshiki Asao",authors:[{id:"106510",title:"Dr.",name:"Toshiki",middleName:null,surname:"Asao",slug:"toshiki-asao",fullName:"Toshiki Asao"},{id:"171564",title:"Dr.",name:"Md",middleName:null,surname:"Asaduzzaman",slug:"md-asaduzzaman",fullName:"Md Asaduzzaman"}]}],onlineFirstChaptersFilter:{topicId:"38",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81669",title:"Recent Techniques and Developments on Cherry Growing in Turkey",slug:"recent-techniques-and-developments-on-cherry-growing-in-turkey",totalDownloads:9,totalDimensionsCites:0,doi:"10.5772/intechopen.104081",abstract:"The wild cherries are mostly seen in the North Anatolian and the Taurus mountains of Turkey. Cherry cultivation is concentrated on the slopes of 1000–1500 m where wild cherries are grown, also at the river valleys or in Izmir and Manisa provinces at the western parts of the country over 100 m elevation with very high quality. Turkey is the leading country in the world on cherry production. Cherry production in Turkey has been performed mostly with ‘0900 Ziraat’ cherry cultivar, which was known as Turkish cherry in Europe. As a result of the studies carried out for fruit cracking and self fertility, Regina, Kordia, Sweet Heart and Lapins cherry cultivars were selected as alternative cherry cultivars to ‘0900 Ziraat’. In 1997, “Turkish National Cherry Working Group” was founded and organized 20 working group meetings until today to solve all the problems of cherries. In these meetings, all the research results were shared and discussed among cherry scientists. Recently, studies on prolonging the cherry season with early and late cherry cultivars to increase the amount of cherry exportation was achieved. For this aim, Sweet Heart at high elevations, Royal Lynn® and Royal Tioga® at subtropical climatic conditions were found to be suitable.",book:{id:"10900",title:"Prunus - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/10900.jpg"},signatures:"Ali Kuden, Ayzin B. Kuden, Songul Comlekcioglu, Burhanettin Imrak and Muhsin Bag"},{id:"77912",title:"Stock Influence on Growth, Morphological and Biochemical Leaf Parameters Prunus domestica L.",slug:"stock-influence-on-growth-morphological-and-biochemical-leaf-parameters-prunus-domestica-l",totalDownloads:90,totalDimensionsCites:0,doi:"10.5772/intechopen.99522",abstract:"Vegetation growth peculiarities and morphological and physical-biochemical features of Prunus domestica L. Utro and Yaichnaya Sinyaya varieties leaves grafted on different growing strength rootstocks were studied. Low-growing clonal rootstocks 140–1 and Novinka decreased the trees growing strength on 15–20% in comparison with strong-growing stocks; medium-growing rootstocks OPA-15-2 and OP-23-23 reduced it on 10%. The longest growing activity and the largest sprouts length was stated on these rootstocks as well, i.e. 1.3–1.4 times more than on other ones. Stable sprouts average length decrease was registered on grafted stocks 140–1 and Novinka. Leaf surface index value on the trees grafted on clonal rootstocks OPA-15-2 and OP-23-23 was on 40% higher than on control, i.e. 4.3 leaves m2/crown projection area m2. Optimal values of total increment, sprouts average length, leaves area and the largest part of physiological-biochemical parameters were stated at medium-growing clonal rootstocks OPA-15-2 and OP-23-23 use. Plum leaves blades were hypostomatic; numerous stomata were located on the abaxial (bottom) side of leaves. Stomata were located in interveinal space irregularly. Stomata length size varied from 14.6 μm (Utro/seedlings) to 22.1 μm (Yaichnaya Sinyaya/OP-23-23). The rootstock has influence on the process of photosynthesis, antioxidant activity, accumulation of minerals and metabolic answerin the leaves.",book:{id:"10900",title:"Prunus - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/10900.jpg"},signatures:"Svetlana Motyleva, Galina Upadysheva, Tatyana Tumaeva and Ivan Kulikov"},{id:"79448",title:"Logistics Chain and Cost Assessment of Pruning-to-Energy Value Chains: Application of Life Cycle Cost Analysis Approach",slug:"logistics-chain-and-cost-assessment-of-pruning-to-energy-value-chains-application-of-life-cycle-cost",totalDownloads:73,totalDimensionsCites:0,doi:"10.5772/intechopen.101428",abstract:"Biomass from agricultural residue has significant potential as renewable energy resource. Therefore, cost-efficient processing and supply of agricultural residues are important to strategically plan and utilize this energy resource. This chapter describes the agricultural pruning to energy (PtE) value chains and presents the life cycle cost analysis (LCCA)-based cost assessment results, focusing on almond and peach tree pruning data obtained from Spain during 2015–2016. Along the main life cycle stages of PtE system, costs of harvesting, off-farm storage, transport, biomass loss, and management of biomass supply chain were considered. In terms of functional unit cost, the life cycle cost (LCC) was calculated to be about 126 €/t for almond PtE and 115 €/t for peach PtE value chain. In both cases, the harvesting stage was found to be cost at hot stage followed by the storage stage. The cost at harvesting stage was about 83% (of 126 €/t) and 82% (of 115 €/t) in the case of almond and peach cases, respectively. Similarly, the share of operational cost was about 74% and 76% for almond and peach cases, respectively. Therefore, more efforts should be made to improve the performance of logistics operations and management of such PtE initiatives.",book:{id:"10900",title:"Prunus - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/10900.jpg"},signatures:"Techane Bosona and Girma Gebresenbet"},{id:"79048",title:"Advances in Breeding of Peach, Plum and Apricot",slug:"advances-in-breeding-of-peach-plum-and-apricot",totalDownloads:121,totalDimensionsCites:0,doi:"10.5772/intechopen.100284",abstract:"Research on the expression of fruit specific genes may allow breeders in the future to selectively manipulate through gene transfer in certain aspects of fruit development/quality in their advanced breeding lines thus reducing the time necessary for cultivar development. This would be particularly useful in breeding programmes, hybridizing standard cultivars with exotic germplasm of low fruit quality. The use of exotic germplasm will be important for the expansion of the peach germplasm base and the development of stress resistant cultivars. More immediate results of research on fruit specific gene expression will provide a better understanding of fruit development and quality. It is required to learn how the differences at the gene level correlate with quality characteristics. With the continued cooperation of fruit biochemists it is expected to obtain a better definition of fruit quality and a better understanding of fruit biochemistry. The potential will exit to generate a range of “anti-sense mutants” i.e. transgenic plants expressing anti-sense gene contstructs that reduce or nullify the effects of the normal gene. The phenotypes of these mutants could help to define the biochemistry, genetics and quality of peach fruit. The development of efficient regeneration and transformation system in peach will be useful not only for the modification of fruit characteristics, but also for the transfer and manipulation of genes affecting stress resistance and other economically important characters.",book:{id:"10900",title:"Prunus - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/10900.jpg"},signatures:"Rimpika and DP Sharma"},{id:"77733",title:"Genetic Diversity in Almond (Prunus dulcis)",slug:"genetic-diversity-in-almond-prunus-dulcis",totalDownloads:96,totalDimensionsCites:0,doi:"10.5772/intechopen.99249",abstract:"Almond (Prunus dulcis), a stone fruit belonging to a family Rosaceae (rose) is broadly cultivated for ornament and fruit. Within this genus, the almond is very much associated with the peach, and these two fruits share the same subgenus the Amygdalus. About 430 species are spread all through the northern temperate regions of the world. The Mediterranean climate region of the Middle East like Turkey and Pakistan eastward to Syria is native to the almond and its related species. Almond is one of the ancient fruit trees known to the Asian as well as European regions with the most primitive proof of cultivation dating about 2000 B.C. Prunus dulcis (Almond) is a nutrient-loaded nut crop. Almond possesses a great genetic diversity due to the genetically controlled self-incompatibility system which can be estimated by a morphological characteristic including molecular markers and isoenzymes with a wide range of marker techniques. Simple sequence repeats (SSR) involving RFLP or SNP are the most commonly used molecular techniques among the DNA-based molecular symbols. Particular agronomic characters, e.g. kernel bitterness or self-compatibility can also be traced by these molecular markers. The direct association between the level of diversity and the basis of the germplasm cannot be understood by the studies of genetic diversity. Genetic diversity cannot be seriously lost by self-compatibility in almonds. The breeding, conservation, and cultivation of wild-growing almonds may similarly advantageous after the genetic diversity research studies (especially those applying molecular markers).",book:{id:"10900",title:"Prunus - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/10900.jpg"},signatures:"Sadia Sana, Naheed Akhter, Fozia Amjum, Samreen Gul Khan and Muhammad Akram"},{id:"78516",title:"Varietal Wealth of Prunus Species",slug:"varietal-wealth-of-prunus-species",totalDownloads:82,totalDimensionsCites:0,doi:"10.5772/intechopen.99048",abstract:"Genus Prunus includes all the stone fruits (peach, nectarine, plum, apricot, almond and cherry) comprise around 98 species and classified under three subgenera namely: Amygdalus (peaches, nectraine and almonds), Prunophora (plums and apricots) and Cerasus (cherries). Genus Prunus have attained a prime position among all the temperate fruit crops as delicious edible drupe, and many species have ornamental values as well. Major species of importance are Prunus persica (peach), Prunus armeniaca (apricot), Prunus salicina (Japanese plum), Prunus domestica (European plum), Prunus americana (American plum), Prunus avium (Sweet cherry), Prunus cerasus (Sour cherry), Prunus dulcis (almond), Prunus ceracifera (Cherry plum), Prunus mira (Behmi), Prunus cerasoides (Wild Himalayan cherry), Prunus mahaleb (Mahaleb cherry) etc. Interspecific hybrids namely: plumcots, pluots and apriums also produce very delicious edible fruits. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRqB9QAK/Profile_Picture_1626163237970",institutionString:null,institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/178706",hash:"",query:{},params:{id:"178706"},fullPath:"/profiles/178706",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()