Technical specifications of the cameras.
\r\n\tThis book aims to address the new developments in the rapidly evolving field of evo-devo in the post genomics era. All recent biological and medical breakthroughs in the evo-devo field are welcomed. Finally, review articles encompassing recent advances, development current and future trends are also more than welcomed.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"4c17ab0c64ce206c75ad6cec64e05737",bookSignature:"Dr. Dimitrios P. Vlachakis, Prof. Elias Eliopoulos and Prof. George P. Chrousos",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10100.jpg",keywords:"genetics, genome, non coding RNAs, post translational modifications, methylation, ancestral genes, horizontal gene transfer, molecular regulators, gene expression levels, transcription factor, chromatin modifications, developmental biology, cell growth, cellular differentiation, stem cells, big data, algorithm design, cloud computing, next generation sequencing",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 20th 2019",dateEndSecondStepPublish:"October 11th 2019",dateEndThirdStepPublish:"December 10th 2019",dateEndFourthStepPublish:"February 28th 2020",dateEndFifthStepPublish:"April 28th 2020",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 years",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"179110",title:"Dr.",name:"Dimitrios",middleName:"P.",surname:"Vlachakis",slug:"dimitrios-vlachakis",fullName:"Dimitrios Vlachakis",profilePictureURL:"https://mts.intechopen.com/storage/users/179110/images/system/179110.jpeg",biography:"Dr. Dimitrios Vlachakis is an Assistant Professor at the Genetics Laboratory at the Biotechnology Department of the Agricultural University of Athens, Greece. He leads the Genetics and Computational Biology Group and his main scientific interests revolve around the investigation of genetic polymorfisms, genetic variability in viral strains and the in silico drug design of novel antiviral and anticancer agents. To date, Dr. Vlachakis has published more than 90 original research articles in international peer-reviewed journals with impact factor, 100+ articles in international conference proceedings, 5 monograph ISBN books, 2 scientific patents and has been on the receiving end of numerous grants and awards. 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A biophysicist/crystallographer by training has considerable experience in biomolecular structure analysis, epitope mapping of protein receptors with experimental and computational methods, protein structure prediction, ligand and drug design, protein design, in silico antibody design and biosoftware development. Prof. Elias Eliopoulos has experience and international reputation on computational protein folding, ab initio and homology modeling of proteins derived from edge gene research and membrane protein modeling. 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Cameras and other integrated additional devices are found in almost every smartphone. Other than these devices, there are tele, macro and fisheye lenses that can easily be integrated to the smartphones. Some of those lens kits are presented in Refs. [1, 2]. Fisheye lenses that are compliant to mobile phones are one of these new equipments. Since fisheye lenses integrated with mobile phones are lightweight and easy to use, they are advantageous. Additionally, these lenses are cost efficient compared to conventional fisheye lenses. The characteristics of Olloclip lens used in this study are presented in Ref. [3]. Cameras on mobile phones are as capable as compact cameras that we use in our daily lives. Smartphone cameras used for acquiring image instead of conventional cameras have opened a new scientific study field. Another scientific study field is that using smartphone cameras together with the developing technologies has given the opportunity to achieve new study fields that have not been made before. Chugh et al. [4] present a detailed survey of methods for detecting road conditions. Smartphone sensors are gaining importance in this field, as they are cost effective and also increase scalability. Analysing from the research activities, it is certain that this area will gain more importance in recent future. The objective of the research is to improve traffic safety through collecting and distributing up‐to‐date road surface condition information using mobile phones [5]. Perttunen et al. [5] present experimental results from real urban driving data that demonstrate the usefulness of the system. To monitor road and traffic conditions in such a setting, Mohan et al. [6] present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. Mohan et al. [6] focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio and/or GPS sensors in these phones to detect potholes, bumps, braking and honking. Wagner et al. [7] present two techniques for natural feature tracking in real‐time on mobile phones and use an approach based on heavily modified state‐of‐the‐art feature descriptors, namely scale invariant feature transform (SIFT) and Ferns. Object‐wise 3D reconstruction is a cardinal problem in computer vision, with much work being dedicated to it throughout recent years. Unlike other approaches, some approaches use global computation, whereas Prisacariu et al. [8] adopt a local computation method related with signed distance transformation and its derivatives. By this method, 3D renderings are quickly obtained by hierarchical ray casting. Real‐time mobile phone performances and speeds faster than 100 fps on PC are achieved by the tracker and GPU acceleration is not required [8]. Tanskanen et al. [9] propose the first dense stereo‐based system for live interactive 3D reconstruction on mobile phones. Pan et al. [10] present a novel system that allows for the generation of a coarse 3D model of the environment within several seconds on mobile smartphones. The contribution of this work is the presentation of a novel approach to generate visually appealing, textured 3D models from a set of at least three panoramic images on mobile phones without the need for remote processing [10]. Wagner et al. [11] present a novel method for the real‐time creation and tracking of panoramic maps on mobile phones. The maps generated with this technique are visually appealing, very accurate and allow drift‐free rotation tracking. Nowadays, smartphones are widely used in the world, and generally, they are equipped with many sensors. Almazan et al. [12] study how powerful the low‐cost embedded Inertial Measurement Unity (IMU) and Global Positioning System (GPS) could become for intelligent vehicles. Main contribution is the method employed to estimate the yaw angle of the smartphone relative to the vehicle co‐ordinate system. The results show that the system achieves high accuracy, the typical error is 1%, and is immune to electromagnetic interference [12]. Recently, mobile phones have become increasingly attractive for augmented reality (AR). The recent advent of GPS and orientation sensors on commodity mobile devices has led to the development of numerous mobile augmented reality (AR) applications and broader public awareness and use of these applications. By using the phone orientation sensor to display the appropriate subset of the panorama, orientation accuracy can be effectively increased and augmentations tightly registered with the background [13]. Kurz and Benhimane [14] presented novel approaches to use the direction of the gravity measured with inertial sensors to improve different parts in the pipeline of handheld AR applications [14]. Amongst all the possible applications, AR systems can be very useful as visualization tools for structural and environmental monitoring. Porzi et al. [15] presented a successful implementation on an android device of an egomotion estimation algorithm by porting the tracking module of parallel tracking and mapping (PTAM). Porzi et al [15] described the development of the egomotion estimation algorithm for an android smartphone. In recent decades, many indoor positioning techniques have been researched and some approaches have even been developed into consumer products. Two devices are selected, the iPhone 3GS and the iPhone 4, to analyse their sensors for usability of an inertial navigation system. A precise Inertial Navigation System (INS) cannot be completely acquired by a strapdown algorithm because of inaccurate and noisy sensors that are used by both the iPhones. In order to enhance the accuracy, several filters were used. Finally, strapdown algorithms were analysed and verified with related testing and best filter combination was found for each of the devices [16]. Burgess et al. [17] expand on previous work by using a multi‐floor model taking into account dampening between floors, and optimize a target function consisting of least squares residuals, to find positions for WiFis and the smartphone measurement locations [17]. Burgess et al. [18] have presented a method for simultaneously mapping the radio environment and positioning several smartphones in multi‐story buildings.
Computer vision applications for mobile phones are gaining increasing attention due to several practical needs resulting from the popularity of digital cameras in today’s mobile phones. Hadid et al. [19] described the task of face detection and authentication in mobile phones, and experimentally analyse a face authentication scheme using Haar‐like features with AdaBoost for face and eye detection and local binary pattern (LBP) approach for face authentication. Shen et al. [20] address the challenges of performing face recognition accurately and efficiently on smartphones by designing a new face recognition algorithm called opti‐sparse representation classification (opti‐SRC). Sparse representation classification (SRC) is a state‐of‐the‐art face recognition algorithm, which has been shown to outperform many classical face recognition algorithms in OpenCV.
Monitoring aquatic environment is of great interest to the ecosystem, marine life and human health [21]. An efficient method for monitoring marine debris is smartphone‐based aquatic robot (SOAR). It is a robotic system having low cost. The aim is to monitor debris in water environment. It contains a smartphone and a robotic fish platform. Robotic fish have a capability to moving through water and smartphone is used to capture images [22]. Another method for detecting debris is Samba. Samba is an aquatic robot that contains a smartphone and a robotic fish platform to monitor harmful marine debris. Using camera of the smartphone, Samba can recognize aquatic debris in dynamic and complex environments [22]. Maindalkar and Ansari [23] present design of aquatic robot for aquatic pollutants monitoring. The android smartphone is integrated with aquatic robot to capture images and to acquire data of different sensors. The implemented design contains CV algorithm for image processing on openCV platform. The real‐time pollutant detection is done with the CV algorithm efficiently [23].
Muaremi [24] investigate the potential of a modern smartphone and a wearable heart rate monitor for assessing affect changes in daily life. Muaremi et al. [24] use smartphone features and heart rate variability (HRV) measures as predictors for building classification models to discriminate among low, moderate and high perceived stress. As smartphones evolve, researchers are studying new techniques to ease the human‐mobile interaction. User interface of mobile phone can be operated by eye tracking and blink detection functions on EyePhone. These results are preliminary, but they suggest that EyePhone is a favourable tool for driving mobile applications with automation [25]. The advent of mobile sensing technology provides a potential solution to the challenge of collecting repeated information about both behaviours and situations such as to detect the type of situation using the sensors built into today’s ubiquitous smartphones [26]. Sandstrom et al. [26] focused on using location sensors to learn the semantics of places, so that we could examine relationships between place, affect and personality. Sensor‐enabled smartphones are opening a new frontier in the development of mobile sensing applications. The recognition of human activities and context from sensor‐data using classification models underpins these emerging applications [27]. The key contribution of community similarity networks (CSN) is that it makes the personalization of classification models practical by significantly lowering the burden to the user through a combination of crowd‐sourced data and leveraging networks that measure the similarity between users. Lu et al. [28] present Jigsaw, a continuous sensing engine for mobile phone applications that require continuous monitoring of human activities and context. Supporting continuous sensing applications on mobile phones is very challenging. Lu et al. [29] propose StressSense for unobtrusively recognizing stress from human voice using smartphones. Lane et al. [30] discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of open issues and challenges emerging in the new area of mobile phone sensing research [30]. Rachuri et al. [31] have presented EmotionSense, a novel system for social psychology study of user emotion based on mobile phones. Rachuri et al. [31] have presented the design of novel components for emotion and speaker recognition based on Gaussian mixture models. The driving vision is a smartphone service, called Mood‐Sense, that can infer its owner’s mood based on information already available in today’s smartphones. In Ref. [32], it is suggested that user mood can be separated into four main types with 91% average accuracy. These results can be obtained with 3 weeks of research data and basic smartphone handling statistics. Although these results are not decisive, they show practicability of mood inference without any microphone and/or camera with bulky power requirements and social interaction [32].
Recently, the calibration methods using display devices such as monitors, tablets or smartphones have come to the forefront [33]. Gruen and Akca [34] report about first experiences in calibration and accuracy validation of mobile phone cameras. Ha et al. [33] propose a novel camera calibration method for defocused images using a smartphone under the assumption that the defocus blur is modelled as a convolution of a sharp image with a Gaussian point spread function (PSF). The effectiveness of the proposed method has been emphasized in several real experiments using a compact display device such as a smartphone [33]. Delaunoy et al. [35] propose a new approach to estimate the geometric extrinsic calibration of all the elements of a smartphone or tablet (such as the screen, the front and the back cameras) by using a planar mirror. Saponaro and Kambhamettu [36] described a method for calibrating a smartphone camera by taking two images at different rotations while tolerating small translations. Ahn et al. [37] were intended to analyse accuracy of smartphone image in determining three‐dimensional location for approximated objects before photo survey system using smartphone is developed, and then evaluate its usability.
Fisheye lenses provide instant wide‐angle images from one point with a single camera. Fisheye optics are placed onto charge couple device (CCD) or complementary metal oxide semiconductor (CMOS) cameras without requiring any complex technology. They do not require an external mirror or rotational device. Thus, these optics are small in size and do not require any maintenance [38]. They have a very short focal length, which produces a hemisphere [39]. By using fisheye lenses, a large area of any surrounding space can be acquired with a single photograph. Therefore, fisheye lenses are useful in most of the applications. In addition to high quality landscape and interior visualizations (e.g. ceiling frescos of historical buildings) in commercial demonstrations or internet presentations, fisheye images are also beneficial for measurement operations [40].
The first fisheye lenses have been created by Hill in 1924 [41], but, they have not been preferred in photogrammetric measurements since they provide images with huge distortions and they do not meet central projection. Using the images obtained from fisheye lens imaging systems in photogrammetric measurement and modeling processes becomes popular in recent years by the help of the development in software and hardware technologies. Later, a significant increase has been seen in terms of volume scientific research on this subject matter. Recently, there have been several academic studies presenting the benefit from fisheye lenses. Fisheye cameras are finding increasing number of applications in surveillance, robotic vision, automotive rear‐view imaging systems, etc. because of their wide‐angle properties [42]. Fisheye lens cameras have also been used during sky observations [43], visual sun compass creation [44], and sunpath diagram derivation [45]. Beekmans et al. [46] present a complete approach for stereo cloud photogrammetry using hemispheric sky imagers. This approach combines calibration, epipolar rectification and block‐based correspondence search for dense fisheye stereo reconstruction for clouds. A novel panoramic imaging system that uses a curved mirror as a simple optical attachment to a fisheye lens is given in Ref. [47]. Streckel et al. [48] describe a visual markerless real‐time tracking system for augmented reality applications. The system uses a firewire camera with a fisheye lens mounted at 10 fps. Brun et al. [49] present a new mobile mapping system mounted on a vehicle to reconstruct outdoor environment in real time. Yamamoto et al. [50] propose a mobile web map interface that is based on a metaphor of the wired fisheye lens. The user can easily navigate through the area surrounding the present location while keeping the focus within the map. These features enable users to find the target quickly. Yamamoto et al. [50] confirmed the advantages of the proposed system by evaluation experiments. The new system will be able to contribute to the novel mobile web map services with fisheye views for mobile terminals such as cellular phones. Ahmad and Lima [51] present a cooperative approach for tracking a moving spherical object in three‐dimensional space by a team of mobile robots equipped with sensors in a highly dynamic environment. Zheng and Li [52] explore the use of a fisheye camera to achieve the scene tunnel acquisition. In Ref. [53], authors have focused on dioptric systems to implement a robot surveillance application for fast and robust tracking of moving objects in dynamic, unknown environments. Another application that uses fisheye lens is a research that examines the use of fisheye lenses as optical sensors on unmanned aerial vehicle (UAV) platform in Queensland Technical University in Australia [54]. Grelsson [55] used a fisheye camera for horizon detection in aerial images. Naruse et al. [56] propose three‐dimensional measurement method of underwater objects using a fisheye stereo camera. In Ref. [57], a novel technique to accurately estimate the global position of a moving car using an omnidirectional camera and untextured three‐dimensional city model is proposed. Today, one of the areas that most frequently benefit from fisheye lenses is applications done in combination with terrestrial laser scanners. Georgantas et al. [58] present a comparison of automatic photogrammetric techniques to terrestrial laser scanning for three‐dimensional modeling of complex interior spaces. The 8 mm fisheye lens that was used allowed us to acquire photos with a global view of the scene and thus with textured zones in every image, which is essential for the scale invariant feature transform (SIFT) algorithm. Image analysis tasks such as 3D reconstruction from endoscopic images require compensation of geometric distortions introduced by the lens system [59]. Hu et al. [60] propose effective pre‐processing techniques to ensure the applicability of face detection tools onto highly distorted fisheye images.
Schneider and Schwalbe [61] present the integration of a geometric model of fisheye lenses and a geometric terrestrial laser scanner model in a bundle block adjustment. Fisheye projection functions are designed such that a greater portion of the scene is projected onto the image sensor on the image plane, at the expense of introducing (often considerable) radial distortion [62]. The fisheye lens camera should be calibrated to be used in applications that require high accuracy [63]. There are different studies in literature, which focus on the calibration of fisheye lenses. Abraham and Forstner [38] presented rigorous mathematical models for the calibration of a stereo system composed of two fisheye lens cameras and for the epipolar rectification of the images acquired by this dual system.
Arfaoui and Thibault [64] have described a method using a compact calibration object for fisheye lens calibration. The setup generated a robust and accurate virtual calibration grid, and the calibration was performed by rotating the camera around two axes. The experimental results and the comparison with a 3D calibration object showed that the virtual grid method is efficient and reliable [64]. Kim and Paik [65] presented a novel 3D simulation method for fisheye lens distortion in a vehicle rear‐view camera. The proposed method creates a geometrically distorted image of an object in 3D space according to the lens specifications. The proposed simulation method can be applied to designing a general optical imaging system for intelligent surveillance as well as a vehicle rear‐view backup camera [65] Torii et al. [66] present a pipeline for camera pose and trajectory estimation, and image stabilization and rectification for dense as well as wide baseline omnidirectional images. The experiments with real data demonstrate the use of the proposed image stabilization method. Five image sequences of a city scene captured by a single hand‐held fisheye lens camera are used as our input [66].
In Ref. [67], Kodak DSC 14 Pro with Nikkor 8 mm fisheye lens is calibrated with an equidistant projection. In addition to decentring, symmetric radial and affinity distortion models, precise mathematical models were used, which were based on stereo‐graphic, equidistant, orthogonal and equisolid‐angle projections. Kannala and Brandt [68] propose a generic camera model, which is suitable for fisheye lens cameras as well as for conventional and wide‐angle lens cameras, and a calibration method for estimating the parameters of the model. Fisheye lenses are not perspective lenses, image resolution in these lenses are not fixed (univocal), illumination is not distributed homogeneously [69]. Upto now, many researchers have considered the relationship between distorted radius and undistorted radius in the image plane ignoring the variation of the angle. Zhu et al. [70] present a fisheye camera model based on the refractive nature of the incoming rays and estimate the model parameters without calibration objects using Micusik’s method [71]. In photogrammetry, the collinearity mathematical model, based on perspective projection combined with lens distortion models, is generally used in the camera calibration process. However, fisheye lenses are designed for the following different spherical projections models such as stereographic, equidistant, orthogonal and equisolid angle [63]. The calibration results of Fuji‐Finepix S3pro camera with Bower‐Samyang 8 mm lens were assessed by the help of precise mathematical models. Bower‐Samyang 8 mm is cheaper than other fisheye lenses and unlike others; it is based on stereographic projection [63].
Most of the fisheye lenses are technically based on equidistant or equisolid‐angle projection. Initially, equisolid‐angle projection geometry is constructed and then diagonal fisheye lenses are constructed. The distortion of the image edges is more significant than fisheye lenses with equidistant projection. The only way to construct orthographic projection geometry is to use sophisticated optical construction. Stereographic projection is not practically realizable [67]. Among the other models proposed, an important one is the equidistant model. The model proposes that the distance between an image point and the centre of radial distortion is proportional to the angle between a corresponding three‐dimensional point, the optical centre and the optical axis [72]. Equidistant fisheye lenses are often used for scientific measurement where the measurement of angles is necessary. Thus, it is also sometimes referred to as an equiangular fisheye lens [73]. Perhaps the most common model is the equidistance projection [68]. Friel et al. [74] use the equidistance projection equation to describe the radial distortion, as this is typically among the most commonly used and inexpensive fisheye lens types. The work described in Ref. [74] shows that it is possible to carry out automatic calibration of fisheye lenses, using information derived from real‐world automotive scenes, and to obtain calibration data to a high degree of accuracy.
The main purpose of this study is to test fisheye lens equipment used with mobile phones. Mobile phone imaging with the additional hardware has been used more popularly not only outside but also in indoor applications. Therefore, hardware properties of this wide‐angle optics will be used in the photogrammetric documentation in the near future for mobile phone imaging. Since fisheye lenses integrated with mobile phones are lightweight and easy to use, they are advantageous. In addition to this advantage, it is experimented whether fisheye lens and mobile phone combination can be used in a photogrammetric way, and if so, what will be the result. In this study, standard calibration of ‘Olloclip 3 in one’ fisheye lens used with iPhone 4S mobile phone and ‘Nikon FC‐E9’ fisheye lens used with Nikon Coolpix8700 are compared based on equidistant model. By using photogrammetric bundle block adjustment, the results of these calibrations are analysed. Geometric properties of these wide‐angle lenses will be more important in the photogrammetric measurement assessment. This study suggests a pre‐calibration process of these kinds of hardware for the photogrammetric process in the test field. In the literature, although there are many geometric camera calibration publications, none of them compares the mobile phone fisheye lens kit with conventional fisheye lens on the fundamentals of photogrammetric measurement assessment. The results of this photogrammetric process are also compared with conventional wide‐angle hardware in this paper.
The second section of this chapter briefly describes fisheye projection models. The third section of this chapter briefly describes equidistant model. The fourth section reports an empirical study for calibration of the combination of iPhone 4S camera with Olloclip 3 in one fisheye lens and Nikon Coolpix8700 camera FC‐09 fisheye lens combination by using equidistant model. The fifth section interprets the results that resulted from the experiment process. The sixth section concludes the study.
Pinhole projection is so called because it preserves the rectilinearity of the projected scene (i.e. straight lines in the scene are projected as straight lines on the image plane). The Pinhole (perspektife) projection is shown in Figure 1. The Pinhole (perspektife) projection mapping function is given in Eq. (1).
Pinhole (perspektife) projection representation.
where f is the distance between the principal point and the image plane, θ is the incident angle (in radians) of the projected ray to the optical axis of the camera and ru is the projected radial distance from the principal point on the image plane. However, for wide field of view (FOV) cameras, under rectilinear projection, the size of the projected image becomes very large, increasing to infinity at an FOV of 180° [62].
Interior orientation parameters (IOPs) can be estimated by a procedure called camera calibration. The perspective bundle, which generated the image, can be reconstructed by this procedure. The principal point co‐ordinates, focal length and coefficient for systematic errors correction (lens distortion: symmetric radial and decentring and affinity) are the IOPs of digital cameras. When additional parameters (IOPs) in Eq. (2) [75] are examined, collinearity equations are the most popular camera calibration method [63, 76].
where, f represents the focal length, and (Xc, Yc, Zc) shows the 3D point co‐ordinates of photogrammetric reference system in Eq. (3); point co‐ordinates of the image are (xf, yf); image point co‐ordinates of the reference system parallel to photogrammetric system are represented as (x\', y\'), this element originates from image centre and principal point (pp) of the co‐ordinates are (xo, yo).
where rij (i and j from 1 to 3) represents rotation matrix elements and with rij, the object can be used in relation to the image reference system; (X, Y, Z) shows any point’s co‐ordinates in the object reference system and (Xcp, Ycp, Zcp) shows perspective centre (PC) in object reference system [63]. Pinhole (perspektife) projection model is not suitable for fisheye lenses. Fisheye lenses instead are usually designed to obey one of the following projections [68]:
In Eqs. (1) and (4)–(7), the angle between optical axis and incoming ray is shown with θ symbol; the distance between image point and principal point is represented with r, and focal length is represented with f. Equidistance projection can be accepted as the most wide‐spread used fisheye lens model. Figure 2a illustrates the schematic description of different projections for the fisheye lens. Figure 2b shows the difference between pinhole lens and fisheye lens. The images acquired with non‐perspective projection are more near to principal point when the results are compared to the results of perspective projection. Therefore, the view angle of fisheye lens is wider than conventional lens. Moreover, actual image surface of fisheye lens presents a hemisphere in accordance with a pinhole lens plane. Thus, projecting the image on surface of the hemisphere into an actual imaging plane results in a deformation of the fisheye lens [77].
The principles for various lenses: (a) shows different lens projections, p, p1, p2, p3 and p4 are respectively perspective projection, stereographic projection, equidistance projection, equisolid angle projection and orthogonal projection; the corresponding distances between image points and the principal point are represented with r, r1, r2, r3 and r4; (b) shows the difference between pinhole lens and fisheye lens. In terms of fisheye lens, perspective image’s projection on the hemisphere surface into the image plane is the actual image.
A wide‐angle lens produces geometric distortion in the radial direction called the barrel distortion, since it compresses the peripheral region to contain a wide angle of view in the image plane. Considering this problem, many researchers have proposed various models to correct the barrel distortion of the wide‐angle lens. A two‐dimensional (2D) approximated barrel distortion model is shown in Figure 3, where an original pixel Pu moves towards the centre at Pd along the radial direction in the image plane [65]. A polynomial model was proposed to approximate various types of wide‐angle lenses using the distortion coefficients. The distance of the distorted pixel Pu is determined by the polynomial equation [65].
Radial distortion in the 2D imaging plane: O represents the image centre, Pu, Pd
In order to model the perfect fisheye lens, scene projections are necessary. These can be defined by two main characteristics. Firstly, field of vision covers 2π steradians, it creates a circular image and the distortions become symmetrical with reference to centre of the image. Secondly, fisheye lens has an infinite depth of field. All objects in the image have a precise focus. Therefore, two postulates, namely the azimuth angle invariability and the equidistant projection rule, govern the formation of non‐linear image distortion. These pre‐suppositions explain the projection of object points into the sensor. They directly affect the eventually developing dewarping algorithm [78].
The azimuth angle invariability, which is the first postulate, determines the projection of points of the plane (which passes through the optical axis that is perpendicular to the sensor plane). The azimuth angle of the object points and their projections onto the sensor remain unchanged due to differences in the object distance or elevation within the content plane [78]. According to Ref. [79], the equidistant lens is ‘preferable for measurement of incidence angles (θ) and azimuth angles. The effect of error of lens position is small, and the linear relation of radial distance (rd) and incidence angle (θ) of a ray from the three‐dimensional point is convenient to analyse’.
The second postulate, the equidistant projection rule, depicts the relationship between radial distance (rd) of an image point on the sensor plane‐zenith (incidence (θ)) angle which is created by the vector of image centre‐world object point in Figure 4. According to this rule, there is a linear relationship between the centre to rd image point radial distance and (θ) zenith angle [78].
Equidistant projection (Equidistant projection,
As the zenith angle varies from 0 to 90°, the radial distance of the corresponding image point varies linearly from 0 to a maximum value R, determined by the modelled sphere’s [78].
(rd) on the image plane, which is the radial distance in equidistant projection, is directly proportional to incident ray’s angle. It is equivalent to arc segment’s length, which is located between the z‐axis and the projection ray of point P on the sphere in Figure 5 [62].
Equidistant fisheye projection function representation.
Thus, the equidistant projection function is given in Eq. (8).
where rd is the fisheye radial distance of a projected point from the centre, f is focal distance and θ represents the incidence angle of a ray which begins from the projected three‐dimensional point into the image plane. In fisheye cameras, following process is performed by the help of this common mapping function. The other mapping functions are stereographic, equisolid and orthogonal [80]. Eq. (9) is derived by substituting arctangent function for θ in Eq. (8). Where ru is the height of the projection on the image plane (the subscript u being used to denote the undistorted projection) [73].
In equidistant projection model, the distorted radial distance on the image plane is linearly expressed as the projected ray’s angle in radians. Moreover, the length of the arc segment between z‐axis and xp is equivalent to the projected distorted distance rd (xp is the intersection point of the projection ray of point X, which has the projection sphere) [73].
Most real optical systems have some undesirable effects, rendering the assumption of the pinhole camera model inaccurate. The most evident of these effects is radial barrel distortion, particularly noticeable in fisheye camera systems, where the level of this distortion is relatively extreme [62]. For most of the applications, the effect of radial distortion can be negligible in normal and narrow field of view (FOV) cameras. However, radial distortion can cause some problems in wide‐angle and fisheye cameras both in terms of visual issues and in the processing of computer vision applications such as object detection, recognition and classification processes [73]. Because of the distortion of the radial lens, points on the image plane are displaced from their ideal position into rectilinear pinhole camera model in a non‐linear way. The movement occurs in a radial axis from distortion centre on the equidistant image plane. The image in the foveal areas has a better resolution because of the displacement factor of fisheye optics. In addition, the peripheral areas of the image satisfy a resolution that decreases non‐linearly [81].
Additional parameters to compensate for deviations of the geometric fisheye model from the physical reality are the same parameters that are applied, as they are in common use, for central perspective lenses [69]. Accordingly, the equidistant projection function with additional parameters is given in Eq. (10).
Due to the particularly high levels of distortion present in fisheye cameras, there have been several alternative models developed [81]. Some models are fisheye transform, field of view, division model, and polynomial model [82]. The work in Ref. [67], investigates the addition of the brown‐parameters to the basic geometric fisheye model to compensate for the remaining ‘systematic effects’ [82].
Three co‐ordinate systems are used in order to define the projection of an object point into a hemispherical fisheye‐dimensional image. These are: the superordinated cartesian object co‐ordinate system (X, Y, Z) and the camera co‐ordinate system (x, y, z) in Figure 6. The image co‐ordinate system (x′, y′) is defined similar to its usual definition in photogrammetric applications. So, the image centre becomes the origin. The x′ and y′ axes are parallel with the x and y axes of camera co‐ordinate system [67]. The geometric concept is based on the dependence of the image radius r′ and the angle of incidence θ [61].
Geometrical model of a fisheye camera.
Object co‐ordinates are transformed into the camera co‐ordinate system. Eq. (11), where X is the co‐ordinate vector in the object co‐ordinate system, x is the co‐ordinate vector in the camera co‐ordinate system, R is the rotation matrix and X0 is the translation between object and camera co‐ordinate system:
The incidence angle θ in the camera co‐ordinate system is defined as follows:
Instead of functions for the image radius r′, functions for the image co‐ordinates x′ and y′ are required. For this purpose, Eq. (13) is applied:
After transformations of the equations described above, the final fisheye projection equations for the image co‐ordinates is derived. The model equations are finally extended by the co‐ordinates of the principal point x′0 and y′0 Eq. (14) and the correction terms Δx′ and Δy′ [Eqs. (15) and (16)], which contain additional parameters to compensate for systematic effects.
Equidistant projection:
where;
A1, A2, and A3 are radial distortion parameters,
B1 and B2 are decentric distortion parameters,
C1 and C2 are horizontal scale factor and shear factor, respectively, and
c is the camera constant, which equals to focal distance.
The characteristics of the cameras and fisheye lenses, which were chosen for the application, are given below.
Nikon Coolpix8700 digital camera has 8 megapixels resolution and CCD sensor. The 8x optical Zoom‐Nikkor lens (f/2.8 – 4.2) offers a focal range of 8.9–71.2 mm [83]. Nikon FC‐E9 fisheye lens: focal length of the camera’s lens reduced to x0.2. Provides approximate 183° (COOLPIX 5700)/190° (COOLPIX 5400) view angle [83].
iPhone 4S camera has 8 megapixels resolution and CMOS sensor. Its focal length is 35 mm [84]. Olloclip 3 in one fisheye lens: The Olloclip is a device providing three different lens options for iPhone, these are wide‐angle, fisheye and macro. The Olloclip with the fisheye lens acquires 180° field of view [85]. Table 1 shows technical specifications of the cameras used in the application.
iPhone 4S | Nikon Coolpix 8700 | |
---|---|---|
Sensor | CMOS 1/3.2′’ sensor size | CCD 2/3″ |
Image resolution | 3264 × 2448 (8.0 MP) | 3264 × 2448 (8.0 MP) |
Focal length* | 4.324602 mm | 9.027620 mm |
Pixel size* | 1.4 µm | 2.7 µm |
Digital zoom values | Up to 5× | Up to 4× |
Aspect ratio | 4:3 | 4:3; 3:2 |
LCD size | 3.5″ | 1.8” |
Technical specifications of the cameras.
Notes: *These are the values obtained after separate calibrations performed before the application in PI3000 software for iPhone 4S and Nikon Coolpix8700 cameras used in the application. The pictures in the resolution of 3264 × 2448 are taken in this focal length.
The Olloclip 3 in one was mounted on the iPhone 4S. Images acquired with iPhone 4S and Olloclip 3 in one fisheye lens combination were captured with a focal distance of 4.28 mm. Images have 3264 × 2448 pixels and 1.4 μm pixel width.
The calibration field used in this study is a satellite antenna having 150 cm diameter with 112 control points on it. It was chosen since it has a smooth digital surface model and it is geometrically similar to the lens surface model. In this way, the analysis of the errors caused by the geometry of the objective and a balanced distribution of the depth differences over the image acquisition line on the surface model is accomplished. Point location accuracy is approximately 30–35 μm. In order to get the determined point location accuracy, a geodesic Wild T3 theodolite was chosen for direction measurements. In total, five serial measurements were made horizontally and vertically with Wild T3 [86]. Used calibration field is complying with self‐calibration model, which is a dish antenna model. Figure 7 shows the images of the calibration field taken by the two camera‐lens combinations. The images of the calibration field were taken with the minimum focal length of the each camera without zooming.
Image of calibration field (Left picture: iphone 4S camera with Olloclip 3 in one; right picture: Nikon Coolpix8700 camera with Nikon FC‐E9 fisheye lens).
The application benefits from the comparison made over iPhone 4S Olloclip 3 in one camera fisheye lens combination with Coolpix8700 camera FC‐09 fisheye lens combination in terms of equidistant fisheye model [Eq. (7)] which gives the best result in bundle block adjustment. In the application, the calibration values derived from equidistant fisheye model for iPhone 4S Olloclip 3 in one camera fisheye lens combination and Coolpix8700 camera FC‐09 fisheye lens combination are compared. Eqs. (8) and (9) use A1, A2, A3, B1, B2, C1, C2 coefficients as calibration parameters. Nine of 112 control points are considered passing points, while 103 of them are full control points. Thirteen images of calibration field were taken by iPhone 4S Olloclip 3 in one camera fisheye lens combination from different locations taking into consideration free network adjustment rules. The same procedure was also applied for Coolpix8700 camera FC‐09 fisheye lens combination. After then, the two‐dimensional image co‐ordinates of 112 control points for 13 images were measured in Pictran D software for iPhone 4S Olloclip 3 in one camera fisheye lens combination. The same procedure was also applied for Coolpix8700 camera FC‐09 fisheye lens combination. The measurements of the two‐dimensional image co‐ordinates in Pictran D software are shown in Figure 8.
Measurement of image co‐ordinates of the fisheye lenses in Pictran D software (Left picture: taken from the combination of iPhone 4S camera with Olloclip 3 in one; right picture: taken from the combination of Nikon Coolpix8700 camera with Nikon FC‐E9 fisheye lens).
The resulting image co‐ordinates were evaluated in bundle block adjustment software developed by Dr. Danilo Schneider in Dresden Technical University in Germany. According to the bundle block adjustment results, Tables 2 and 3 were acquired.
iPhone 4S with Olloclip 3 in one | NIKON Coolpix8700 with NIKON FC‐E9 | |
---|---|---|
Sigma0 | 0.00099 | 0.00163 |
Convergence | 0 | 0 |
Max. iteration | 100 | 100 |
Required iteration | 18 | 32 |
Calculation time | 3.57 sn | 6.41 sn |
Unknown | 396 | 396 |
Observations | 2868 | 2838 |
Calibration results of two different fisheye in bundle adjustment software.
iPhone 4S with Olloclip 3 in one (mm) | Nikon Coolpix8700 with Nikon FC‐E9 (mm) | |||||
---|---|---|---|---|---|---|
Value | rms | Significance | Value | rms | Significance | |
ck | −2.23950000 | 0.00161000 | 8 | −1.69005000 | 0.00203000 | 8 |
x0 | 0.05886000 | 0.00397000 | 8 | 0.03987000 | 0.00259000 | 8 |
y0 | 0.10471000 | 0.00399000 | 8 | 0.11442000 | 0.00240000 | 8 |
A1 | 0.00080228 | 0.00027401 | 8 | 0.00118750 | 0.00038919 | 6 |
A2 | −0.00386240 | 0.00007329 | 8 | −0.00067814 | 0.00005062 | 8 |
B1 | −0.00105490 | 0.00016903 | 8 | −0.00026227 | 0.00012548 | 4 |
B2 | −0.00137450 | 0.00015347 | 8 | 0.00021274 | 0.00011927 | 3 |
C1 | 0.00050562 | 0.00032783 | 1 | −0.00009505 | 0.00029182 | 1 |
C2 | −0.00038251 | 0.00030980 | 6 | 0.00031644 | 0.00028458 | 1 |
Calibration parameters calculated for equidistant model.
The results of bundle block adjustment calculation by the software for 13 images captured with iPhone 4S camera and Olloclip 3 in one are given, respectively; the sigma0 value is 0.00099 and the pixel size is 0.0022 mm. The results of bundle block adjustment calculations by the software for 13 images captured with Nikon Coolpix8700 camera and FC‐09 fisheye lens are given, respectively; the sigma0 value is 0.00163 and the pixel size is 0.0022 mm. Sigma0 is the root mean square (rms) of measurements for image co‐ordinates after bundle block adjustment. Table 2 shows the resulting values in both of the calibrations. According to software’s post‐adjustment outputs, additionally, projection point parameters (X0, Y0, Z0 and omega, phi, kappa) of the 13 images and object points’ significance for both of the camera and lens combination are 8, which is 99.9%. (Software’ significance values are 1: no significance, 2: 80%, 3: 90%, 4: 95%, 5: 98%, 6: 99%, 7: 99.8%, 8: 99.9% high significance) [87].
Figure 9 shows actual positions of three‐dimensional co‐ordinates of calibration field obtained by adjustment results and projection points of each of the 13 images that come from both of the calibration files acquired after adjustment.
Three‐dimensional position of projection points with regard to antenna; three‐dimensional co‐ordinates of the camera as the result of balancing (left image shows values for iPhone 4S camera with Olloclip 3 in one; and right image shows values for Nikon Coolpix8700 camera with Nikon FC‐E9 fisheye lens).
At the end of the application designed for testing, numerical values of calibration parameters and rms of those parameters that were calculated according to equidistant model were compared between the ‘Olloclip 3 in one’ fisheye lens used with iPhone 4S mobile phone and standard ‘Nikon FC‐E9’ fisheye lens used in Nikon Coolpix8700. This comparison is given in Table 3 and the resulting graphics are shown in Figures 10 and 11.
Distortion parameters for two different camera‐fisheye lens combinations.
Rms values of distortion parameters for two different camera‐fisheye lens combinations.
Since A3 distorsion parameter was a so small value that can be ignored, it was not analysed and written in Table 3 [87]. Table 3 shows that the significance values of the iPhone are higher than that of Nikon because of smaller pixel (it is given in Table 1 in Section 3) structure of iPhone’s camera.
When Figure 11 is examined, it is seen that distortion parameters of iPhone 4S camera and Olloclip 3 in one fisheye lens equipment is larger, although it has a larger focal length. It does not show a significant difference than Nikon Coolpix8700 camera and FC‐09 fisheye lens equipment. (ck: focal length after calibration process, x0: image co‐ordinate of principle point in X direction, y0: image co‐ordinate of principle point in Y direction).
Figures 10 and 11 illustrate that when x0, y0 principal image point co‐ordinate values and focal lengths of two different fisheye lenses are ignored for the cameras with same resolution and same pixel size, a meaningful approximation is obtained. In consideration of these results, current technology developed for Olloclip 3 in one lens that is improved for mobile phones, is particularly great. Nikon FC‐E9 mounted on bulky Nikon Coolpix8700 is difficult to use. Olloclip 3 in one lens mounted on iPhone 4S is considered to be used in studies done with photogrammetric fisheye lens instead of Nikon FC‐E9. The conclusion part compares the advantages and disadvantages of two different fisheye images.
Nine of 112 control points are considered as passing points, while 103 of them are full control points. One hundred and three point co‐ordinates from testing area are considered errorless and used in bundle block adjustment. Three‐dimensional position data derived from the bundle block adjustment are compared to the errorless points. Nikon Coolpix8700 camera and FC‐09 fisheye lens combination give accuracy on 85 points under the sub‐pixel level. IPhone 4S camera and Olloclip 3 in one fisheye lens combination gives accuracy on 89 points under the sub‐pixel level. This means that there is 82.52% accuracy for Nikon Coolpix8700 camera and FC‐09 fisheye lens combination and 86.40% accuracy for iPhone 4S camera and Olloclip 3 in one fisheye lens combination. The co‐ordinates of three‐dimensional object co‐ordinates of 103 points are subtracted from the points that derived from bundle block adjustment process. If the difference values are greater than sub‐pixel level in any axis then they are eliminated consequently. Figures 12 and 13 are depicted from the obtained differences, respectively, for iPhone 4S camera and Olloclip 3 in one fisheye lens combination and Nikon Coolpix8700 camera and FC‐09 fisheye lens combination. Delta X is the difference between three‐dimensional point co‐ordinate that measured before adjustment in X direction and three‐dimensional point co‐ordinate, which derived after adjustment in X direction. Delta Y and delta Z were calculated similarly.
Subpixel graphic for the combination of iPhone 4S camera with Olloclip 3 in one fisheye lens.
Subpixel graphic for the combination of Nikon Coolpix8700 camera with Nikon FC‐E9 fisheye lens.
The standard deviations of co‐ordinate differences have been calculated for three different axes from the data contributing to depict Figures 12 and 13. The standard deviation values on the X, Y and Z axes are 0.763, 0.558 and 0.638 mm, respectively, for Olloclip 3 in one lens kit. By using similar derivation, the standard deviation values on the X, Y and Z axes are 0.748, 0.699 and 0.517 mm, respectively, for Nikon FC‐09 lens kit. As presented, the standard deviation values of identical axes are found approximately close to each other from the calculations. Moreover, when the distribution of the co‐ordinate differences were evaluated for three identical axes (X, Y, Z) of these two kinds of lens kits, it was calculated that they have the same maximum difference value which is approximately 2 mm. From these graphics, root mean square error of point positions has been determined as 3.556 mm for Olloclip 3 in one and 3.401 mm for Nikon FC‐09 lens kit. These values show us the internal reliability of these two kinds of fisheye lens kits is similar for three‐dimensional point co‐ordinate determination.
When the difference of image co‐ordinates derived before and after adjustment are analysed in vectorial form, Figures 14 and 15 are achieved for the two different fisheye lenses. If the difference between the measured images co‐ordinates and the adjusted image co‐ordinates are analysed in vectorial form, Figures 14 and 15 are achieved for the two different fisheye lenses. As can be seen from these two figures, residuals decrease while approaching to the image principal point for the two fisheye lens cameras, but increase proportionally to the sides due to distortion. The standard deviation results of the above‐mentioned experiment are 0.000814 for iPhone 4S camera and Olloclip 3 in one fisheye lens combination and 0.000890 for Nikon Coolpix8700 camera and FC‐09 fisheye lens combination. It can be said that according to above explained results, although there is not a significant difference, iPhone 4S camera and Olloclip 3 in one fisheye lens combination has lower distortion on the total image surface than Nikon Coolpix8700 camera and FC‐09 fisheye lens combination. Therefore, iPhone 4S camera and Olloclip 3 in one fisheye lens combination could also be used for photogrammetric applications instead of Nikon Coolpix8700 camera and FC‐09 fisheye lens combination.
Image co‐ordinates residuals for iPhone 4S camera and Olloclip 3 in one fisheye lens combination (between measured and after adjustment).
Image co‐ordinates residuals for Nikon Coolpix8700 camera and FC‐09 fisheye lens combination (between measured and after adjustment).
Advantages and disadvantages of using fisheye lenses for the above‐mentioned equipments can be listed as follows:
Advantages
IPhone 4S camera and Olloclip 3 in one fisheye lens equipment is lightweight and it is much more easy to use.
As given in Table 3 in Section 3, focal distance of iPhone 4S camera and Olloclip 3 in one fisheye lens equipment is larger than focal distance of Nikon Coolpix8700 camera and FC‐09 fisheye lens equipment. The rms of the larger focal distance is smaller than the other one.
There is no significant difference between the image centre point co‐ordinates of iPhone 4S camera and Olloclip 3 in one fisheye lens equipment and Nikon Coolpix8700 camera and FC‐09 fisheye lens.
Disadvantages
Nikon Coolpix8700 camera and FC‐09 fisheye lens equipment are heavier and much more difficult to use.
Since focal distance of iPhone 4S camera and Olloclip 3 in one fisheye equipment is larger than Nikon Coolpix8700 camera and FC‐09 fisheye lens equipment, the resulting distortion parameters are expected to be smaller and when distortion parameters of both equipments are compared to each other, there happens to be a stable result that exceeds the expectations.
As given in Table 3 in Section 3, mean square error values for image central point of 4S camera and Olloclip 3 in one fisheye lens equipment are higher than mean square error values of Nikon Coolpix8700 camera and FC‐09 fisheye lens equipment. Therefore, Nikon Coolpix8700 camera and FC‐09 fisheye lens equipment can be considered to be more stable.
IPhone 4S camera and Olloclip 3 in one fisheye lens equipment should be tested in a photogrammetric study and the results should be interpreted in the light of these data.
The main purpose of this study is to test fisheye lens equipment used with mobile phones. In this study, the performance of Olloclip 3 in one fisheye lens used with iPhone 4S mobile phone and Nikon FC‐E9 fisheye lens used with Nikon Coolpix8700 camera is analysed comparing the calibration results based on an equidistant model. The resolution of the cameras is the same for these two kinds of hardware. The co‐ordinates of image centre point were found approximately close to each other from the calculations for these two kinds of hardware. It was seen that the calibration results of Olloclip 3 in one fisheye lens used with iPhone 4S mobile phone have not showed statistical significant difference results compared to Nikon FC‐E9 fisheye lens used with Nikon Coolpix8700. In addition, it was seen from the results of this study that Olloclip 3 in one fisheye lens has larger focal length than the other. This experimental study shows that Olloclip 3 in one fisheye lens developed for mobile phones has at least the similar characteristics with classic fisheye lenses.
Smartphones and fish eye lens are very popular devices in developing computer technologies. The use of fisheye lenses having big distortion was limited in the past, but today use of advanced computer software ease the solution of distortion problem. Therefore, fisheye lenses became the mostly studied devices and issues in photogrammetric fieldwork. Additionally, the use of fisheye lenses together with smartphones has opened new research areas. The dimensions of fisheye lenses used with smartphones are getting smaller and the prices are reducing. Moreover, as verified in this study, the accuracy of fisheye lenses used in smartphones is better than conventional fisheye lenses. The use of smartphones with fisheye lenses will give the possibility of practical applications to ordinary users in the near future.
Cumhur Sahin was born in 1977. He got his bachelor’s degree in 2001 in Istanbul Technical University in Turkey. He completed his post‐graduation in 2004 with Master of Science in Geodesy and Photogrammetry Engineering in Gebze Institute of Technology in Turkey. He obtained his doctorate in 2011 in Geodesy and Photogrammetry Engineering in Yildiz Technical University in Turkey. He worked as a research assistant in Gebze Institute of Technology from 2001 to 2014 in Turkey. He has been working as a research assistant in Gebze Technical University in Turkey since 2014.
The global population is expected to cross 9.1 billion by the year 2050 and food production is projected to rise to 70% to feed this growing population [1]. Many of the fastest-growing populations are in developing countries, several of which are already facing moderate or severe food insecurity and a shortfall in food supply. One in every six children suffer from hunger in developing countries [2] and the proportion of undernourishment has been steadily increasing since 2015 [3]. The increasing trend globally of food insecurity attests to the fact that severe food deprivation or hunger is a real threat, and this scenario nullifies the ambitious “zero hunger target” by 2030. The severity of “food insecurity” underscores the immense challenge in attaining safe, nutritious, and sufficient food for all people [3]. Tackling problems of food insecurity demand intensive food production. However, increasing food production alone will not be a viable solution to achieve the “zero hunger target” by 2030 or for meeting the growing demand for food.
The pre-harvest and post-harvest issues combined with insect infestation represent a very strong limitation in optimal food production, causing mass losses of grains. After harvest, food grains undergo a series of processes such as threshing, cleaning, drying, storage, processing, and transportation before it reaches the consumer. It has been identified that food losses in the post-harvesting chain start at the time of harvest and continue up to food marketing at the consumer’s end [4, 5, 6]. Grain losses may also take place due to technical limitations such as inadequate stock management facilities, improper packaging, and insufficient infrastructure.
In many countries, 15% of food grains are lost during or after harvest [7]. The Food and Agricultural Organization (FAO) estimated post-harvest grain loss at 40% and cereal loss at 30% in India [8]. The post-harvest losses account for on-farm, processing, and storage loss. Studies attribute massive grain loss in developing countries to manual operations in different stages of harvesting, which causes 15% loss on the field, 13–20% loss at processing, and 15–25% storage loss [9]. Several studies show that insects are the main contributor to storage loss in the food supply chain [10, 11, 12], which accounts for 10–20% of storage loss [13].
A diverse community of stored product species are associated with different environments where farmers store grains and cereals; from farm bins to processing facilities, to feed mills, to flour mills, to retailer stores [14, 15, 16]. Among this complex pest system, 600 species from Coleoptera and 70 species from Lepidoptera can cause substantial losses by eroding the quality of grains [17]. Coleoptera is the largest order of insects with over 250,000 described species and contains in its fold some of the most notorious stored grain pests. In these,
The red flour beetle,
The adult females of
Thus, a wide variety of insecticides has been applied as a primary strategy for
In the Indian context, the first cases of insecticide resistance were reported in 1971 by Bhatia et al. [71] who found
Similarly, malathion resistance level in Indian populations of
The emergence and spread of insecticide resistance in an insect population is a slow and gradual evolutionary process. Following the initial exposure to the insecticide, there is a latent period in which resistance genes are segregated and linked with other genes that contribute favorable conditions for resistance development. During the evolution of resistance under insecticide selection pressure, the target species show a noticeable increase of tolerance to the pesticide. In the next stage, insecticide resistance slowly develops, followed by a period of rapid development, during which many factors influence the selection of resistance to insecticides. Rapidly developing resistance results in explosive population growth of the pests in stored products that become almost impossible to control. It is challenging to detect the resistance mechanism because they emerge over evolutionary time. Many key factors such as intensive application of insecticides, control operations, mode of inheritance of resistance genes, change in fitness of individuals, and genetic background of insects influence resistance [79]. Despite species diversity and chemical diversity of insecticides, only three mechanisms are known to cause insecticide resistance in
The advances in genomic research (e.g., transcriptomic sequencing and whole-genome sequencing) have made significant progress in understanding resistance mechanisms such as metabolic resistance, penetration resistance, and knockdown resistance in
Researchers around the world have begun to evaluate the symbiotic associations in different pest populations, how they interact with their hosts and whether they have the potential to detoxify insecticides. Interestingly, bacterial symbionts have been involved in insecticide degradation and resistance development in some insect pests, weeds, and nematodes. There are a growing number of reports where pest resistance to insecticides is not only due to the mechanisms within the pest genome but also due to the organisms in the microbiome community [84]. However, the microbial communities inhabiting
Insecticides such as organophosphates, carbamates, and pyrethroids produce neurotoxicity by inhibiting the enzyme acetylcholine esterase associated with the central nervous system [85, 86, 87, 88]. These insecticides also affect other target sites such as voltage-gated sodium channels (VGSC) and gamma aminobutyric acid (GABA) receptors in the insect nervous system [89]. The DDT and pyrethroid insecticides primarily target VGSC in the nervous system [90]. Several potential insecticides such as cyclodienes and fipronil bind to the GABA receptor and block the receptor function [91]. Most commonly used insecticides primarily target different receptors on the nervous system (Figure 1).
Diagrammatic representation of pre and post synaptic neurons, showing the different target sites of most commonly used insecticide classes. Source: Adapted and modified from [
Insecticide-resistant insects perform normal neurological functions despite the presence of insecticide because they have evolved insensitive acetylcholine receptors which provide resistance to organophosphate and carbamate insecticides. The reduced sensitivity of acetylcholinesterase to OP and carbamate insecticides has been studied in many resistant insect species of agricultural and veterinary importance [92, 93, 94, 95, 96]. The reduced target site sensitivity is a result of altered insecticide target molecules. There are mainly four types of target site insensitivity mechanisms observed in various insect species. These include a) Altered Acetylcholinesterase (AChE) resistance mechanism, which provides resistance to organophosphates and carbamates b) Knockdown resistance (
Acetylcholinesterase (AchE) is a vital enzyme required for regulating the neurotransmitter acetylcholine (ACh). It terminates the synaptic transmission by hydrolyzing acetylcholine into acetate and choline at cholinergic synapses in insects [99]. The inhibition of AchE increases the concentration of the acetylcholine at the synaptic cleft, which leads to a prolonged binding of ACh to its postsynaptic receptor. The high quantity of acetylcholine at the postsynaptic receptor causes neuroexcitation and produces intoxication symptoms such as tremors, convulsions, and eventually paralysis-related death. This enzyme is a target site of organophosphates and carbamates insecticides, which are bound to a serine residue on the active site of AchEs and convert the AchEs into their non-functional form. This causes the accumulation of acetylcholine at the nerve endings and disrupts nerve activity, resulting in paralysis and the death of insects [100].
Several Organophosphorous compounds have been used to protect agricultural commodities from insect infestation. But most of the insects have developed resistance against these insecticides due to insensitive AchE. Target insensitivity of AchE to insecticides occurs through the mutations in the active site of “
Recent evidence for resistance-conferring mutations in
Over the years, Pyrethroids have come to be the most sought-after class of insecticides for pest control in commercial and household environments because of their affordable and durable qualities [123]. However, their utility has been limited by the widespread development of insecticide resistance in many major pests. Pyrethroids are synthetic derivatives of pyrethrin, and the pyrethroids were classified into two groups namely class I and class II based on their physical characteristics and knockdown effect against insects. Class I pyrethroids contain a basic structure of cyclopropane carboxylic ester. These compounds include permethrin, resmethrin, phenothrin, bifenthrin, allethrin, tefluthrin, and tetramethrin. Class II pyrethroids contain a cyano group and these compounds include cypermethrin, deltamethrin, cyhalothrin, fenvalerate, cyfluthrin, fenpropathrin, flumethrin. The toxicity of pyrethroids was found to be 2250 times higher in insects than mammals due to their increased sodium channel sensitivity, lower body temperature, and smaller structure [124]. When an insect is intoxicated with non-cyano pyrethroids (class I), it produces strong excitatory action and tremors on the nervous system. The cyano pyrethroids trigger a quite different action, which includes salivation and choreoathetosis. It has been suggested that poisoning symptoms differ based on the cyano or non-cyano pyrethroids [125]. The pyrethrin and pyrethroid insecticides primarily target the VGSC in the nervous system. Pyrethroids and DDT produce their toxicity by binding onto the voltage-gated channels in axonal membranes, altering their gating properties, and the channels remain open for a long time. This causes a prolonged sodium influx, thereby depolarizing the axonal membrane and stimulating the neurons to produce repetitive discharges, finally resulting in paralysis [90, 126]. In insects, modification of voltage-gated sodium channel structure by point mutation or substitution causes insensitivity and reduces the binding affinity of the insecticides to protein.
Knockdown resistance (
The GABA ionotrophic receptor of the neuron membrane is formed by the oligomerization of five subunits around a central pore and each subunit possesses a large N terminal domain and four membrane-spanning domains (M1–M4). Several potential insecticides such as cyclodienes and fipronil stick to the M2 membrane-spanning a region of GABA receptor as competitive inhibitors, which prevent the chloride uptake of the GABA ion channel. The inhibition of GABA stimulated chloride uptake enhances the firing of nerve impulses in insects, which initiates lethal effects on insects [145]. However, the mutations or modifications in the molecular structure of the GABA ion channel influence the activity of insecticides. The mutated GABA receptor becomes insensitive to the insecticides at varying levels and this insensitivity has the potential to increase resistance in an insect species.
Such a resistant modification was first characterized in the GABA receptor subunit gene
In addition, genome sequencing advancements have facilitated the detection of multiple
The most common resistance mechanism in insects is the metabolic detoxification mechanism, enabling the insect to degrade or sequester the insecticides faster before releasing their toxic effect. This resistance mechanism allows insects to overproduce the enzymes mainly cytochrome
Several studies showed that resistant insects possess generally higher levels of
Recent years have witnessed the rapid evolution of insecticide resistance due to their continuous exposure. However, the resistance mechanism in insects is not fully understood, and the evolution of resistance to insecticides in
Increased detoxification by cytochrome
The glutathione S tranferase (
To gain insights on the regulatory, functional, and biological significance of
Carboxylesterases are ubiquitous enzymes involved in the detoxification of ester-containing xenobiotics. They are members of the esterase family of enzymes and have been isolated from all living organisms. As their name suggests, they are involved in hydrolysis reactions and convert the carboxyl esters into carboxylic acid and alcohol. Hydrolysis of the ester bond includes hydrolysis of a diverse range of phospho, thio, carboxylic, and other ester substrates. For carboxylesterases, the hydrolysis reaction is accomplished by 2 steps- first, the nucleophilic attack of oxygen of a serine residue on the carbonyl group of the substrate, removing the alcohol product, and generating relatively stable acyl enzymes. Second, a water molecule acts as an intermediary and makes a nucleophilic attack to remove the acid product of the reaction and produce the free enzyme. This reaction mechanism causes insecticide resistance in many insect species. As a key component of the detoxification mechanism, esterases have focused on the research of xenobiotic metabolism and resistance. The expression of carboxylesterases was significantly upregulated in the organophosphorous resistant
The occurrence of multiple mechanisms in an insect develops a very high level of resistance and in the case of
Reduced penetration resistance is uncommon and little is known about its workings in insects. Reduced penetration is also called cuticle resistance that reduces the dose of the insecticide reaching into the insect’s body and in all probability strongly associated with insecticide resistance. Normally contact insecticide penetrates through the insect cuticle and reaches the target site for action [172]. The cuticle is composed mainly of two different components, chitin, and cuticular protein, and the three functional layers of the cuticle consist of the outermost envelope, protein-rich epicuticle, and chitin-rich procuticle [211, 212]. Cuticular barriers develop resistance in insects by altering the cuticular thickness or by changing the cuticular composition [213, 214] or remodeling the cuticle by the high occurrence of cuticular proteins. The overexpression of laccases and ABC transporters has been reported to be involved in the compositional change of cuticle, which increases insects’ tolerance to insecticides in the environment. Arkane et al. [215] revealed the association between the cuticle tanning and the expression profile of
Researchers have been previously using bioassays, genetic and biochemical techniques to study the resistance mechanisms in
Overview of types of resistance mechanisms in
Sl. No. | Insecticide | Type of Resistance | Mechanism | References |
---|---|---|---|---|
1 | Cyclodiene | Point mutations in the gene Resistance to dieldrin ( | Target site insensitivity | [138, 151] |
2 | Pirimiphos-methyl and bifenthrin | Elevated activity of lipases, esterases, and laccase2 | Combination of reduced cuticular penetrance and metabolic detoxification | [208] |
3 | Dichlorvos, malathion, carbaryl, and carbofuran | Mutations in | Reduced insensitivity of AChE | [108] |
4 | Malathion | Elevated activity of carboxylesterase | Metabolic detoxification | [217, 218, 219, 220] |
5 | Malathion | Elevated activity of glutathione transferase | Metabolic detoxification | [221] |
6 | Deltamethrin | Elevated activity of | Metabolic detoxification | [144] |
7 | Phosphine | Elevated activity of | Metabolic detoxification | [222, 223] |
8 | Phosphine | Elevated activity of | Metabolic detoxification | [187] |
9 | Permethrin | Elevated activity of | Metabolic detoxification | [187] |
10 | Cypermethrin, Permethrin and Cyhalothrin | Elevated activity of | Metabolic detoxification | [163] |
11 | Phosphine | Elevated activity of | Metabolic detoxification | [164] |
12 | Dichlorvos and carbofuran | Elevated activity of | Metabolic detoxification | [186] |
13 | Carbofuran or dichlorvos | Induction of carboxylesterase | Metabolic detoxification | [165] |
14 | Pirimiphos-methyl | Reduced cuticular penetration | Cuticular penetration resistance mechanism | [224] |
15 | Phoxim | Elevated activity of | Metabolic detoxification | [201] |
Studies performed in
Recent years have seen a sharp increase in the study of insect microbiome, its crucial role in metabolic detoxification, and modulation of host immune responses. In some insect hosts, the symbiotic association appears to be causal for insecticide degradation, whereas, in others, studies suggest that it is mediated by physiological trade-offs [225]. In addition, the relationship between microbial community and insecticide resistance differs greatly and is context-dependent [225]. Several studies have established a causal connection between the fitness-enhancing symbionts and insecticide resistance in the bean bug,
Increased access to a rapidly advancing metagenomic approach facilitated the understanding of the role of microbial communities in insecticide resistance, and several studies hint at this association. The initial research that isolated the bacteria and fungi from different life stages of
Multiple reports indicate that
Different toxicity bioassays were used to measure resistance in the early stage in a cost-efficient manner. The conventional bioassay that is used to diagnose resistance involves collecting
The fully sequenced and annotated genome of
Resistance research on this beetle further improved by the most advanced approach Clustered regularly interspaced short palindromic repeats (CRISPR) system [237]. Recently, CRISPR is the best available method on vogue in order to explore the functional genes relevant to resistance in
Further, on the basis of gene expression, knockdown and genome editing studies, we could generate information on insecticide resistance levels of pest populations in our country. This information would offer a unique opportunity for overcoming or delaying resistance in
Insecticide resistance poses a major threat to global pest control efforts and elucidating the underlying mechanisms is critical for effective pest management. Most of the important pests of stored products have evolved resistance to commonly used insecticides the world over.
This review attempts to address critical questions around how insecticide resistance emerges in
The availability of whole-genome sequence and applications of RNAi have made significant progress in understanding resistance mechanisms in
The comprehensive genomic and transcriptomic analysis have improved the identification of the key genes encoding detoxifying enzymes such as CY
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The traditional healer provides health care services based on culture, religious background, knowledge, attitudes, and beliefs that are prevalent in his community. Illness is regarded as having both natural and supernatural causes and thus must be treated by both physical and spiritual means, using divination, incantations, animal sacrifice, exorcism, and herbs. Herbal medicine is the cornerstone of traditional medicine but may include minerals and animal parts. The adjustment is ok, but may be replaced with –‘ Herbal medicine was once termed primitive by western medicine but through scientific investigations there is a better understanding of its therapeutic activities such that many pharmaceuticals have been modeled on phytochemicals derived from it. Major obstacles to the use of African medicinal plants are their poor quality control and safety. Traditional medical practices are still shrouded with much secrecy, with few reports or documentations of adverse reactions. 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The main global health organizations have incorporated patient safety in their review of work practices. The data provided by the medical laboratories have a direct impact on patient safety and a fault in any of processes such as strategic, operational and support, could affect it. To provide appreciate and reliable data to the physicians, it is important to emphasize the need to design risk management plan in the laboratory. Failure Mode and Effect Analysis (FMEA) is an efficient technique for error detection and reduction. Technical Committee of the International Organization for Standardization (ISO) licensed a technical specification for medical laboratories suggesting FMEA as a method for prospective risk analysis of high-risk processes. FMEA model helps to identify quality failures, their effects and risks with their reduction/elimination, which depends on severity, probability and detection. Applying FMEA in clinical approaches can lead to a significant reduction of the risk priority number (RPN).",book:{id:"9808",slug:"contemporary-topics-in-patient-safety-volume-1",title:"Contemporary Topics in Patient Safety",fullTitle:"Contemporary Topics in Patient Safety - Volume 1"},signatures:"Hoda Sabati, Amin Mohsenzadeh and Nooshin Khelghati",authors:[{id:"340486",title:"M.Sc.",name:"Hoda",middleName:null,surname:"Sabati",slug:"hoda-sabati",fullName:"Hoda Sabati"},{id:"348872",title:"M.Sc.",name:"Amin",middleName:null,surname:"Mohsenzadeh",slug:"amin-mohsenzadeh",fullName:"Amin Mohsenzadeh"},{id:"348874",title:"MSc.",name:"Nooshin",middleName:null,surname:"Khelghati",slug:"nooshin-khelghati",fullName:"Nooshin Khelghati"}]},{id:"65467",title:"Anesthesia Management for Large-Volume Liposuction",slug:"anesthesia-management-for-large-volume-liposuction",totalDownloads:5965,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The apparent easiness with which liposuction is performed favors that patients, young surgeons, and anesthesiologists without experience in this field ignore the many events that occur during this procedure. Liposuction is a procedure to improve the body contour and not a surgery to reduce weight, although recently people who have failed in their plans to lose weight look at liposuction as a means to contour their body figure. Tumescent liposuction of large volumes requires a meticulous selection of each patient; their preoperative evaluation and perioperative management are essential to obtain the expected results. The various techniques of general anesthesia are the most recommended and should be monitored in the usual way, as well as monitoring the total doses of infiltrated local anesthetics to avoid systemic toxicity. The management of intravenous fluids is controversial, but the current trend is the restricted use of hydrosaline solutions. The most feared complications are deep vein thrombosis, pulmonary thromboembolism, fat embolism, lung edema, hypothermia, infections and even death. 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This chapter summarizes the principal epigenetic dysregulation mechanisms involved in the MPM pathogenesis. The most studied mechanism is hypermethylation mediated by DNA methyltransferases (DNMTs) in different tumor suppressor genes, and the relation with asbestos fiber exposure, which represents the main risk factor. Physiopathology is related to chronic inflammation mediated by free radicals that produce chromosomal alterations, genomic instability, increased angiogenesis, and tumor invasion factors like EGFR, FGFR, TGF-B, and PDGF. Additionally, independent methylation pathways that produce gene silencing such as polycomb complex and SWI/SNF mutation are reviewed. Finally, other mechanisms are described such as hypomethylation with imprint loss and pro-oncogenic gene activation that induce immunological responses, as well as acetylation, deacetylation, and demethylation in the chromatin and histone context.",book:{id:"10831",title:"Mesothelioma - Diagnostics, Treatment and Basic Research",coverURL:"https://cdn.intechopen.com/books/images_new/10831.jpg"},signatures:"Aldo Manuel Alvarez Moran, Pablo Alejandro Ávila Sánchez, Jorge Alejandro Torres Ríos and Lorena Vega Castillo"},{id:"82331",title:"Diseases of Medicinal Plants Cultivated in Karnataka and Their Management",slug:"diseases-of-medicinal-plants-cultivated-in-karnataka-and-their-management",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.104632",abstract:"A broad spectrum of fungal diseases infecting selected 10 medicinal plants surveyed in Karnataka, India, was studied in the present research. We present a detailed review on previously reported as well as our present investigation’s details of fungal diseases, etiology, symptoms, and its management. Some of the commonly observed diseases are Anthracnose disease, Blight disease, Leaf spot, Root rot, Powdery mildew, Downy mildew, and Wilt disease. The detailed analysis of medicinal plants revealed that the medicinal plants are susceptible to diverse fungal phytopathogens. Therefore, sustainable management of the diseases is necessary for the successful cultivation of disease-free medicinal plants.",book:{id:"11299",title:"Medicinal Plants",coverURL:"https://cdn.intechopen.com/books/images_new/11299.jpg"},signatures:"P. Swetha and R. Sundararaj"},{id:"82446",title:"Possibility of Using a VR System as an Action Observation Therapeutical Technique",slug:"possibility-of-using-a-vr-system-as-an-action-observation-therapeutical-technique",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.105579",abstract:"In recent years, 3D virtual reality (VR) systems are increasingly finding their way into biomedical applications. Nevertheless, in most cases a 3D VR is being used as an interactive system (such as Xbox Kinect or Playstation VR). These interactive systems, however effective they may have proven, not only limit use of 3D VR in patients incapable to engage in these systems due to their physical or mental disability, but also put significant requirements on medical institutions for an equipment, medical personal, and therefore institutional budget. In this article, we are proposing a 3D VR as an stand-alone action observation training device, which could limit requirements associated with abovementioned interactive systems due to its capability to stimulate a mirror neuron system of human brain, while adding minimal demands on both patient and medical facility. Research studies that confirm activity in the motor cortex will be described. We focus on the literature that describes theories, models, and experimental studies dealing with the effects of motion observations that are involved in the control and final performance of motor skills.",book:{id:"11832",title:"Neurorehabilitation and Physical Therapy",coverURL:"https://cdn.intechopen.com/books/images_new/11832.jpg"},signatures:"Jaroslav Langer, Monika Šorfová and David Ravnik"},{id:"82430",title:"Hepatocellular Carcinoma",slug:"hepatocellular-carcinoma",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.105473",abstract:"Over 1 million cases of liver cancer are estimated to occur by 2025, making it a global health challenge. In almost 90% of cases of liver cancer, it is hepatocellular carcinoma (HCC). The main risk factors for HCC development are infection with hepatitis B and C viruses, although nonalcoholic steatohepatitis (NASH) associated with metabolic syndrome or diabetes mellitus is becoming more prevalent in the West. The molecular pathogenesis of nonalcoholic steatohepatitis-associated HCC is unique. A quarter of all HCCs present with mutations that are potentially actionable but have not yet been translated into clinical practice. In the advanced stages of the disease, systemic therapy is expected to be administered 50–60% of the time to HCC patients. In phase III trials, six systemic therapies have been approved (atezolizumab plus bevacizumab, sorafenib, lenvatinib, regorafenib, cabozantinib, and ramucirumab), and new trials are evaluating combination therapies, such as checkpoint inhibitors and tyrosine kinase inhibitors or anti-VEGF therapies. The findings of these clinical trials are expected to alter the landscape of managing HCC at all stages of the disease.",book:{id:"11265",title:"Hepatotoxicity",coverURL:"https://cdn.intechopen.com/books/images_new/11265.jpg"},signatures:"Rahmat Adetutu Adisa and Lateef Adegboyega Sulaimon"},{id:"82038",title:"Role of Skin Substitutes in Burn Wound Reconstruction",slug:"role-of-skin-substitutes-in-burn-wound-reconstruction",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.105179",abstract:"Skin substitutes have modernised burn wound reconstruction since their use was first pioneered by Burke and Yannas in the 1980s. Skin substitutes offer a solution to the problem of insufficient autologous skin graft availability in major burn wound closure. A growing body of evidence supports the role of skin substitutes in both acute major burns and secondary burn scar resurfacing. Classification of skin substitutes has become increasingly complex given the large variety of synthetic and biologic dermal matrices now available as the result of ongoing advances in regenerative medicine techniques. Classification systems are required to assist clinicians with selection and comparison of outcomes across a wide diversity of skin substitutes. Professor John Greenwood, invented, designed and developed one such dermal substitute, \\'Biodegradable Temporising Matrix\\', which is approved for use across the globe for reconstruction of major burns and complex wounds. This chapter provides a review of available classification systems for skin substitutes with a summary of the latest evidence in relation to their role and impact on burn wound outcomes. Future developments toward the elusive ‘ideal’ skin substitute may be possible through ongoing research efforts focused on clinical translation of modern skin tissue engineering techniques for burn wound reconstruction.",book:{id:"11723",title:"Wound Healing - Recent Advances and Future Opportunities",coverURL:"https://cdn.intechopen.com/books/images_new/11723.jpg"},signatures:"Elizabeth Concannon, Lindsay Damkat-Thomas, Patrick Coghlan and John E. Greenwood"},{id:"82422",title:"Mesenchymal Stem/Stromal Cells in Allergic Disease Management",slug:"mesenchymal-stem-stromal-cells-in-allergic-disease-management",totalDownloads:4,totalDimensionsCites:0,doi:"10.5772/intechopen.105763",abstract:"Allergic diseases are a clump of disorders caused by protective or harmful immune responses to specific exogenous stimulations. To date, the worldwide prevalence of allergic diseases has caused considerable perplex to patients and guardians physically and mentally. Despite the significant advances in preclinical investigation and clinical practice, yet the effective treatment strategies for allergic diseases are far from satisfaction. State-of-the-art renewal has highlighted the involvement of mesenchymal stem/stromal cell (MSC)-based cytotherapy for various allergic disease management including atopic dermatitis, pediatric asthma, allergic rhinitis, and urticaria, which largely attributes to the unique immunomodulatory properties and mode of action via autocrine and paracrine, direct- or trans-differentiation. In this chapter, we mainly focus on the latest updates of MSC-based investigations upon allergic disease administration as well as the concomitant prospective and challenges, which will provide overwhelming new references for MSC-based cytotherapy in regenerative medicine.",book:{id:"11849",title:"Allergic Disease - New Developments in Diagnosis and Therapy",coverURL:"https://cdn.intechopen.com/books/images_new/11849.jpg"},signatures:"Leisheng Zhang, Zhongchao Han and Xiaowei Gao"}],onlineFirstChaptersTotal:713},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:317,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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\r\n\tEducation and Human Development is an interdisciplinary research area that aims to shed light on topics related to both learning and development. This Series is intended for researchers, practitioners, and students who are interested in understanding more about these fields and their applications.
",coverUrl:"https://cdn.intechopen.com/series/covers/23.jpg",latestPublicationDate:"June 25th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:0,editor:{id:"280770",title:"Dr.",name:"Katherine K.M.",middleName:null,surname:"Stavropoulos",slug:"katherine-k.m.-stavropoulos",fullName:"Katherine K.M. Stavropoulos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRdFuQAK/Profile_Picture_2022-05-24T09:03:48.jpg",biography:"Katherine Stavropoulos received her BA in Psychology from Trinity College, in Connecticut, USA. Dr. Stavropoulos received her Ph.D. in Experimental Psychology from the University of California, San Diego. She completed her postdoctoral work at the Yale Child Study Center with Dr. James McPartland. Dr. Stavropoulos’ doctoral dissertation explored neural correlates of reward anticipation to social versus nonsocial stimuli in children with and without autism spectrum disorders (ASD). 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This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. 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. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. 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://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/91912",hash:"",query:{},params:{id:"91912"},fullPath:"/profiles/91912",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)}()