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Speckle Reduction in Projection Systems

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ISBN: 9783866444188 Year: Pages: XIV, 153 p. DOI: 10.5445/KSP/1000012434 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Technology (General)
Added to DOAB on : 2019-07-30 20:01:59
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A speckle pattern is a quasi-random interference pattern which typically emerges when lasers are used as illumination sources in projection applications and which severely degrades the image quality. Since in most projection applications high speckle disturbance is not tolerable, speckle reduction is a major issue. This work gives an introduction into the theoretical description of speckle and investigates different practical methods for speckle reduction in laser projection systems.

Optomechatronics

ISBN: 9783038420019 9783038420088 Year: Pages: 276 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Added to DOAB on : 2016-05-20 15:03:42
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The field of optomechatronics provides synergistic effects of optics, mechanics and electronics for efficient sensor development. Optical sensors for the measurement of mechanical quantities, equipped with appropriate electronic signal (pre)processing means have a wide range of applications, from surface testing, stress monitoring, thin film analysis to biochemical sensing. The aim of this special issue is to provide an overview of actual research and innovative applications of optomechatronics in sensors. Papers addressing, inter alia, optical sensor principles, fiber-optic sensors, electronic speckle pattern interferometry, surface analysis, thin film measurement, FGB sensors, and biochemical sensors are provided.

Selected Papers from IEEE ICKII 2018

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ISBN: 9783039212736 / 9783039212743 Year: Pages: 82 DOI: 10.3390/books978-3-03921-274-3 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Electronic engineering and design innovation are both academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation via electronic engineering includes electrical circuits and devices, computer science and engineering, communications and information processing, and electrical engineering communications. The Special Issue selected excellent papers presented at the International Conference on Knowledge Innovation and Invention 2018 (IEEE ICKII 2018) on the topic of electronics and their applications. This conference was held on Jeju Island, South Korea, 23–27 July 2018, and it provided a unified communication platform for researchers from all over the world. The main goal of this Special Issue titled “Selected papers from IEEE ICKII 2018” is to discover new scientific knowledge relevant to the topic of electronics and their applications.

Ultrasound B-mode Imaging: Beamforming and Image Formation Techniques

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ISBN: 9783039211999 / 9783039212002 Year: Pages: 146 DOI: 10.3390/books978-3-03921-200-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Ultrasound medical imaging stands out among the other diagnostic imaging modalities for its patient-friendliness, high temporal resolution, low cost, and absence of ionizing radiation. On the other hand, it may still suffer from limited detail level, low signal-to-noise ratio, and narrow field-of-view. In the last decade, new beamforming and image reconstruction techniques have emerged which aim at improving resolution, contrast, and clutter suppression, especially in difficult-to-image patients. Nevertheless, achieving a higher image quality is of the utmost importance in diagnostic ultrasound medical imaging, and further developments are still indispensable. From this point of view, a crucial role can be played by novel beamforming techniques as well as by non-conventional image formation techniques (e.g., advanced transmission strategies, and compounding, coded, and harmonic imaging). This Special Issue includes novel contributions on both ultrasound beamforming and image formation techniques, particularly addressed at improving B-mode image quality and related diagnostic content. This indeed represents a hot topic in the ultrasound imaging community, and further active research in this field is expected, where many challenges still persist.

Liquid Crystal on Silicon Devices: Modeling and Advanced Spatial Light Modulation Applications

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ISBN: 9783039218288 / 9783039218295 Year: Pages: 172 DOI: 10.3390/books978-3-03921-829-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:16
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Liquid Crystal on Silicon (LCoS) has become one of the most widespread technologies for spatial light modulation in optics and photonics applications. These reflective microdisplays are composed of a high-performance silicon complementary metal oxide semiconductor (CMOS) backplane, which controls the light-modulating properties of the liquid crystal layer. State-of-the-art LCoS microdisplays may exhibit a very small pixel pitch (below 4 ?m), a very large number of pixels (resolutions larger than 4K), and high fill factors (larger than 90%). They modulate illumination sources covering the UV, visible, and far IR. LCoS are used not only as displays but also as polarization, amplitude, and phase-only spatial light modulators, where they achieve full phase modulation. Due to their excellent modulating properties and high degree of flexibility, they are found in all sorts of spatial light modulation applications, such as in LCOS-based display systems for augmented and virtual reality, true holographic displays, digital holography, diffractive optical elements, superresolution optical systems, beam-steering devices, holographic optical traps, and quantum optical computing. In order to fulfil the requirements in this extensive range of applications, specific models and characterization techniques are proposed. These devices may exhibit a number of degradation effects such as interpixel cross-talk and fringing field, and time flicker, which may also depend on the analog or digital backplane of the corresponding LCoS device. The use of appropriate characterization and compensation techniques is then necessary.

Pheochromocytoma (PHEO) and Paraganglioma (PGL)

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ISBN: 9783039216543 / 9783039216550 Year: Pages: 380 DOI: 10.3390/books978-3-03921-655-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Medicine (General)
Added to DOAB on : 2019-12-09 11:49:16
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This book outlines some new advances in genetics, clinical evaluation, localization, therapy (newly including immunotherapy) of pheochromocytoma and paraganglioma including their metastatic counterparts. Well-known and experienced clinicians and scientists contributed to this book to include some novel approaches to these tumors. This book will serve to various health care professionals from different subspecialties, but mainly oncologists, endocrinologists, endocrine surgeons, pediatricians, and radiologists. This book shows that the field of pheochromocytoma/paraganglioma is evolving and a significant progress has been made in last 5 years requiring that health care professionals and scientists will learns new information and implement it in their clinical practice or scientific work, respectively. This book should not be missed by anybody who is focusing on neuroendocrine tumors, their newest evaluation and treatment.

Keywords

pheochromocytoma --- paraganglioma --- adrenocortical carcinoma --- adrenal tumor --- pan-cancer analysis --- neural crest --- neuroendocrine --- paraganglioma --- head and neck --- radiotherapy --- 18F-FDOPA --- PET --- GTV --- SDHB --- SDHD --- mortality --- paraganglioma --- pheochromocytoma --- radiofrequency ablation --- cryoablation --- percutaneous ethanol injection --- neuroendocrine tumor --- minimally invasive procedure --- percutaneous ablation --- PASS --- GAPP --- histology --- meta-analysis --- paraganglioma --- pheochromocytoma --- carotid body --- angiogenesis --- mitochondria --- neural crest --- neurogenesis --- paraganglioma --- stem-like tumor cells --- vasculogenesis --- xenograft --- pheochromocytoma --- catecholamine --- global longitudinal strain --- speckle-tracking echocardiography --- subclinical systolic dysfunction --- pheochromocytoma --- paraganglioma --- neuroendocrine tumor --- targeted therapy --- therapy resistance --- FGF21 --- pheochromocytoma --- paraganglioma --- diabetes mellitus --- obesity --- energy metabolism --- calorimetry --- chromogranin A --- metanephrines --- pheochromocytoma --- paraganglioma --- hypoxia --- pseudohypoxia --- spheroids --- HIF --- EPAS1 --- catecholamine --- pheochromocytoma and paraganglioma --- phosphorylation tyrosine hydroxylase --- dog --- pheochromocytoma --- paraganglioma --- SDHB --- SDHD --- mutation --- chromosomal alteration --- comparative genomics --- pheochromocytoma --- paraganglioma --- metastatic --- immunotherapy --- innate immunity --- adaptive immunity --- toll-like receptor --- pathogen-associated molecular patterns --- neutrophil --- T cell --- pheochromocytoma --- paraganglioma --- hypertension --- blood pressure variability --- average real variability --- weighted standard deviation --- paraganglioma --- somatostatinoma --- polycythemia --- EPAS1 --- transgenic mice --- erythropoietin --- pheochromocytoma --- paraganglioma --- TCA cycle --- germline mutation --- metastatic OR malignant pheochromocytoma --- paraganglioma --- ectopic secretion --- lL-6 --- normetanephrines --- VHL --- NF1 --- EPAS1 --- hypoxia-inducible factor --- inflammation --- radiosensitization --- succinate dehydrogenase --- mouse pheochromocytoma cells --- immunohistochemistry --- fluorescence imaging --- pheochromocytoma --- paraganglioma --- next-generation sequencing --- sporadic --- hereditary --- CNV detection --- pheochromocytoma --- paraganglioma --- PET-CT --- 11C-hydroxy-ephedrine --- adrenal incidentaloma --- pheochromocytoma --- paraganglioma --- 177Lu-DOTATATE --- peptide receptor radiotherapy --- PRRT --- neuroendocrine tumor --- NET --- PCC --- PGL --- postoperative --- pheochromocytoma --- hypertension --- hypotension --- arrhythmia --- PPGL --- catecholamines --- adrenomedullary function --- n/a

Learning to Understand Remote Sensing Images

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ISBN: 9783038976844 / 9783038976851 Year: Volume: 1 Pages: 426 DOI: 10.3390/books978-3-03897-685-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

Learning to Understand Remote Sensing Images

Author:
ISBN: 9783038976981 / 9783038976998 Year: Volume: 2 Pages: 376 DOI: 10.3390/books978-3-03897-699-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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Abstract

With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

Sensors in Agriculture

Author:
ISBN: 9783038974123 / 9783038974130 Year: Pages: 346 DOI: 10.3390/books978-3-03897-413-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Agriculture requires technical solutions for increasing production while lessening environmental impact by reducing the application of agro-chemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have enormously facilitated the attainment of these objectives, removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture and ‘smart farming’ are emerging areas where sensor-based technologies play an important role. Farmers, researchers, and technical manufacturers are joining their efforts to find efficient solutions, improvements in production, and reductions in costs. This book brings together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed.

Keywords

wireless sensor network (WSN) --- Wi-SUN --- vine --- mandarin orange --- thermal image --- fluorescent measurement --- X-ray fluorescence spectroscopy --- visible and near-infrared reflectance spectroscopy --- heavy metal contamination --- spectral pre-processing --- feature selection --- machine-learning --- LiDAR --- light-beam --- plant localization --- Kinect --- leaf area index --- radiative transfer model --- neural networks --- GF-1 satellite --- wide field view --- big data --- geo-information --- plant phenotyping --- grapevine breeding --- Vitis vinifera --- ambient intelligence --- wireless sensor --- fuzzy logic --- smart irrigation --- virtual organizations of agents --- CIE-Lab --- precision plant protection --- optical sensor --- weed control --- classification --- NIR hyperspectral imaging --- chemometrics analysis --- weeds --- UAS --- RPAS --- one-class --- machine learning --- remote sensing --- geoinformatics --- plant disease --- pest --- deep convolutional neural networks --- real-time processing --- detection --- hyperspectral imaging --- soil type classification --- total nitrogen --- texture features --- data fusion --- Fusarium --- near-infrared --- spectroscopy --- hulled barely --- partial least squares-discriminant analysis --- remote sensing --- precision agriculture --- crop monitoring --- data fusion --- speckle --- diffusion --- scattering --- biological sensing --- apparent soil electrical conductivity --- ECa-directed soil sampling --- electromagnetic induction --- proximal sensor --- response surface sampling --- salt tolerance --- boron tolerance --- soil mapping --- soil salinity --- spatial variability --- irrigation --- energy balance --- water management --- semi-arid regions --- on-line vis-NIR measurement --- total nitrogen --- total carbon --- spiking --- gradient boosted machines --- artificial neural networks --- random forests --- rice --- striped stem-borer --- hyperspectral imaging --- texture feature --- data fusion --- greenhouse --- wireless sensor network --- data fusion --- dynamic weight --- dataset --- agriculture --- obstacle detection --- computer vision --- cameras --- stereo imaging --- thermal imaging --- LiDAR --- radar --- object tracking --- crop area --- remote sensing image classification --- area frame sampling --- stratification --- regression estimator --- agriculture --- meat spoilage --- vegetable oil --- quality assessment --- electronic nose --- electrochemical sensors --- spectral analysis --- feature selection --- genetic algorithms --- classification --- vegetation indices --- vineyard --- diseases --- spatial data --- sensor --- data fusion --- change of support --- geostatistics --- precision agriculture --- management zones --- event detection --- back propagation model --- multivariate water quality parameters --- time-series data --- spatial-temporal model --- connected dominating set --- water supply network --- SS-OCT --- Capsicum annuum --- germination --- salt concentration --- deep learning --- clover-grass --- precision agriculture --- dry matter composition --- proximity sensing --- 3D reconstruction --- RGB-D sensor --- crop inspection platform --- water depth sensors --- soil moisture sensors --- temperature sensors --- rice field monitoring --- irrigation --- silage --- packing density --- moisture content --- compound sensor --- simultaneous measurement --- birth sensor --- bovine embedded hardware --- ambient intelligence --- virtual organizations of agents --- Fusarium --- near infrared --- discrimination --- hulled barely --- naked barley --- wheat --- dielectric probe --- apple shelf-life --- dielectric dispersion --- electronic nose --- pest scouting --- pest management --- gas sensor --- noninvasive detection --- nitrogen --- near infrared sensors --- drying temperature --- SPA-MLR --- PLS --- CARS --- hyperspectral camera --- handheld --- sensor evaluation --- case studies --- soil --- moisture --- sensor --- landslide --- rice leaves --- chromium content --- laser-induced breakdown spectroscopy --- laser wavelength --- preprocessing methods --- agricultural land --- field crops --- land cover --- photograph-grid method --- remote sensing --- data validation and calibration --- mobile app --- wireless sensor networks (WSN) --- energy efficiency --- distributed systems --- processing of sensed data --- WSN distribution algorithms --- recognition patterns --- agriculture

Sensors in Agriculture

Author:
ISBN: 9783038977445 / 9783038977452 Year: Pages: 354 DOI: 10.3390/books978-3-03897-745-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-06-26 08:44:06
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Abstract

Agriculture requires technical solutions for increasing production while lessening environmental impact by reducing the application of agro-chemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have enormously facilitated the attainment of these objectives, removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture and ‘smart farming’ are emerging areas where sensor-based technologies play an important role. Farmers, researchers, and technical manufacturers are joining their efforts to find efficient solutions, improvements in production, and reductions in costs. This book brings together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed.

Keywords

wireless sensor network (WSN) --- Wi-SUN --- vine --- mandarin orange --- thermal image --- fluorescent measurement --- X-ray fluorescence spectroscopy --- visible and near-infrared reflectance spectroscopy --- heavy metal contamination --- spectral pre-processing --- feature selection --- machine-learning --- LiDAR --- light-beam --- plant localization --- Kinect --- leaf area index --- radiative transfer model --- neural networks --- GF-1 satellite --- wide field view --- big data --- geo-information --- plant phenotyping --- grapevine breeding --- Vitis vinifera --- ambient intelligence --- wireless sensor --- fuzzy logic --- smart irrigation --- virtual organizations of agents --- CIE-Lab --- precision plant protection --- optical sensor --- weed control --- classification --- NIR hyperspectral imaging --- chemometrics analysis --- weeds --- UAS --- RPAS --- one-class --- machine learning --- remote sensing --- geoinformatics --- plant disease --- pest --- deep convolutional neural networks --- real-time processing --- detection --- hyperspectral imaging --- soil type classification --- total nitrogen --- texture features --- data fusion --- Fusarium --- near-infrared --- spectroscopy --- hulled barely --- partial least squares-discriminant analysis --- remote sensing --- precision agriculture --- crop monitoring --- data fusion --- speckle --- diffusion --- scattering --- biological sensing --- apparent soil electrical conductivity --- ECa-directed soil sampling --- electromagnetic induction --- proximal sensor --- response surface sampling --- salt tolerance --- boron tolerance --- soil mapping --- soil salinity --- spatial variability --- irrigation --- energy balance --- water management --- semi-arid regions --- on-line vis-NIR measurement --- total nitrogen --- total carbon --- spiking --- gradient boosted machines --- artificial neural networks --- random forests --- rice --- striped stem-borer --- hyperspectral imaging --- texture feature --- data fusion --- greenhouse --- wireless sensor network --- data fusion --- dynamic weight --- dataset --- agriculture --- obstacle detection --- computer vision --- cameras --- stereo imaging --- thermal imaging --- LiDAR --- radar --- object tracking --- crop area --- remote sensing image classification --- area frame sampling --- stratification --- regression estimator --- agriculture --- meat spoilage --- vegetable oil --- quality assessment --- electronic nose --- electrochemical sensors --- spectral analysis --- feature selection --- genetic algorithms --- classification --- vegetation indices --- vineyard --- diseases --- spatial data --- sensor --- data fusion --- change of support --- geostatistics --- precision agriculture --- management zones --- event detection --- back propagation model --- multivariate water quality parameters --- time-series data --- spatial-temporal model --- connected dominating set --- water supply network --- SS-OCT --- Capsicum annuum --- germination --- salt concentration --- deep learning --- clover-grass --- precision agriculture --- dry matter composition --- proximity sensing --- 3D reconstruction --- RGB-D sensor --- crop inspection platform --- water depth sensors --- soil moisture sensors --- temperature sensors --- rice field monitoring --- irrigation --- silage --- packing density --- moisture content --- compound sensor --- simultaneous measurement --- birth sensor --- bovine embedded hardware --- ambient intelligence --- virtual organizations of agents --- Fusarium --- near infrared --- discrimination --- hulled barely --- naked barley --- wheat --- dielectric probe --- apple shelf-life --- dielectric dispersion --- electronic nose --- pest scouting --- pest management --- gas sensor --- noninvasive detection --- nitrogen --- near infrared sensors --- drying temperature --- SPA-MLR --- PLS --- CARS --- hyperspectral camera --- handheld --- sensor evaluation --- case studies --- soil --- moisture --- sensor --- landslide --- rice leaves --- chromium content --- laser-induced breakdown spectroscopy --- laser wavelength --- preprocessing methods --- agricultural land --- field crops --- land cover --- photograph-grid method --- remote sensing --- data validation and calibration --- mobile app --- wireless sensor networks (WSN) --- energy efficiency --- distributed systems --- processing of sensed data --- WSN distribution algorithms --- recognition patterns --- agriculture

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