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Quality and Production of Forage

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ISBN: 9783039216741 9783039216758 Year: Pages: 148 DOI: 10.3390/books978-3-03921-675-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Biology --- Agriculture (General)
Added to DOAB on : 2019-12-09 11:49:16
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The book ""Quality and Production of Forage"" is intended to keep readers updatedon the developments occurring in this field. As it is apparent that livestockanimals are important throughout the world because of the meat, milk and eggthey produce, knowledge about the forages available to animals must also beconsidered for increased production, quality and efficiency. This book providesinformation that readers will find considerably invaluable about forage feeds, suchas grass, legumes, and straw. The book is composed of ten papers, focusing on awide range of research activities and topics that feature the following concepts offorage: the effect of conservation method on forage protein value; Microbial thecomposition and mycotoxin content in forage; genetic diversity of forages; timelysowing to maximize yield for both grain and biomass; ensiling treatments onforage quality; the qualitative characteristics of different accessions of new foragespecies; forage policy influencing feed costs; feeding forage on animal health;high-protein tropical forages as alternative sources for poultry; impact of foragesin poultry diet and significance of forages in sustainable poultry productionsystems. This book will be an invaluable reference for students and professionalsin agricultural science and grassland and animal husbandry researches.

Remote Sensing of Above Ground Biomass

Authors: ---
ISBN: 9783039212095 9783039212101 Year: Pages: 264 DOI: 10.3390/books978-3-03921-210-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local–regional–global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring.

Keywords

multi-angle remote sensing --- forest structure information --- vegetation indices --- forest biomass --- Bidirectional Reflectance Distribution Factor --- biomass --- yield --- AquaCrop model --- spectral index --- particle swarm optimization --- winter wheat --- TerraSAR-X --- Landsat --- pasture biomass --- Wambiana grazing trial --- foliage projective cover --- fractional vegetation cover --- ALOS2 --- mixed forest --- biomass --- lidar --- NDVI --- grass biomass --- SPLSR --- vegetation indices --- estimation accuracy --- pasture biomass --- ground-based remote sensing --- ultrasonic sensor --- field spectrometry --- sensor fusion --- short grass --- alpine grassland conservation --- anthropogenic disturbance --- ecological policies --- climate change --- grazing exclusion --- grazing management --- regional sustainability --- rice --- biomass --- dry matter index --- chlorophyll index --- CIRed-edge --- NDLMA --- forest above ground biomass (AGB) --- random forest --- mapping --- alpine meadow grassland --- above-ground biomass --- inversion model --- error analysis --- applicability evaluation --- Land Surface Phenology --- wetlands --- above ground biomass --- NDVI --- MODIS time series --- food security --- Sahel --- Niger --- rangeland productivity --- livestock --- MODIS --- NDVI --- aboveground biomass --- Atriplex nummularia --- carbon mitigation --- carbon inventory --- forage crops --- remote sensing --- vegetation index --- stem volume --- dry biomass --- conifer --- broadleaves --- light detection and ranging (LiDAR) --- regression analysis --- correlation coefficient --- n/a

Sensors in Agriculture

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ISBN: 9783038974123 9783038974130 Year: Pages: 346 DOI: 10.3390/books978-3-03897-413-0 Language: English
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: English
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

Advances in Near Infrared Spectroscopy and Related Computational Methods

Authors: ---
ISBN: 9783039280520 9783039280537 Year: Pages: 496 DOI: 10.3390/books978-3-03928-053-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Chemistry (General) --- Analytical Chemistry
Added to DOAB on : 2020-01-30 16:39:46
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In the last few decades, near-infrared (NIR) spectroscopy has distinguished itself as one of the most rapidly advancing spectroscopic techniques. Mainly known as an analytical tool useful for sample characterization and content quantification, NIR spectroscopy is essential in various other fields, e.g. NIR imaging techniques in biophotonics, medical applications or used for characterization of food products. Its contribution in basic science and physical chemistry should be noted as well, e.g. in exploration of the nature of molecular vibrations or intermolecular interactions. One of the current development trends involves the miniaturization and simplification of instrumentation, creating prospects for the spread of NIR spectrometers at a consumer level in the form of smartphone attachments—a breakthrough not yet accomplished by any other analytical technique. A growing diversity in the related methods and applications has led to a dispersion of these contributions among disparate scientific communities. The aim of this Special Issue was to bring together the communities that may perceive NIR spectroscopy from different perspectives. It resulted in 30 contributions presenting the latest advances in the methodologies essential in near-infrared spectroscopy in a variety of applications.

Keywords

hyperspectral imaging --- variety discrimination --- Chrysanthemum --- deep convolutional neural network --- DNA --- FTIR spectroscopy --- rapid identification --- PLS-DA --- animal origin --- near-infrared hyperspectral imaging --- raisins --- support vector machine --- pixel-wise --- object-wise --- maize kernel --- hyperspectral imaging technology --- accelerated aging --- principal component analysis --- support vector machine model --- standard germination tests --- blackberries --- Rubus fructicosus --- phenolics --- carotenoids --- bioanalytical applications --- near infrared --- chemometrics --- VIS/NIR hyperspectral imaging --- corn seed --- classification --- freeze-damaged --- image processing --- imaging visualization --- wavelength selection --- NIR spectroscopy --- binary dragonfly algorithm --- ensemble learning --- quantitative analysis modeling --- NIR --- SCiO --- pocket-sized spectrometer --- cheese --- fat --- moisture --- multivariate data analysis --- Fourier-transform near-infrared spectroscopy --- glucose --- fructose --- dry matter --- partial least square regression --- Ewing sarcoma --- Fourier transform infrared spectroscopy --- FTIR --- chemotherapy --- bone cancer --- calibration transfer --- NIR spectroscopy --- PLS --- quantitative analysis model --- melamine --- FT-IR --- NIR spectroscopy --- quantum chemical calculation --- anharmonic calculation --- overtones --- combination bands --- near infrared spectroscopy --- Trichosanthis Fructus --- geographical origin --- chemometric techniques --- crude drugs --- prepared slices --- support vector machine-discriminant analysis --- near-infrared fluorescence --- fluorescent probes --- Zn(II) --- di-(2-picolyl)amine --- living cells --- cellular imaging --- near-infrared (NIR) spectroscopy --- calibration transfer --- affine invariance --- multivariate calibration --- partial least squares (PLS) --- NIR --- direct model transferability --- MicroNIR™ --- SVM --- hier-SVM --- SIMCA --- PLS-DA --- TreeBagger --- PLS --- calibration transfer --- agriculture --- photonics --- imaging --- spectral imaging --- spectroscopy --- handheld near-infrared spectroscopy --- pasta/sauce blends --- partial least squares calibration --- nutritional parameters --- bootstrapping soft shrinkage --- partial least squares --- extra virgin olive oil --- adulteration --- FT-NIR spectroscopy --- near-infrared spectroscopy --- ethanol --- anharmonic quantum mechanical calculations --- isotopic substitution --- overtones --- combinations bands --- seeds vitality --- rice seeds --- near-infrared spectroscopy --- hyperspectral image --- discriminant analysis --- near-infrared spectroscopy --- counter propagation artificial neural network --- detection --- auxiliary diagnosis --- BRAF V600E mutation --- colorectal cancer --- tissue --- paraffin-embedded --- deparaffinized --- stained --- ultra-high performance liquid chromatography --- Folin–Ciocalteu --- total hydroxycinnamic derivatives --- phytoextraction --- near-infrared spectroscopy --- origin traceability --- data fusion --- Paris polyphylla var. yunnanensis --- Fourier transform mid-infrared spectroscopy --- near-infrared spectroscopy --- aquaphotomics --- water --- light --- near infrared spectroscopy --- water-mirror approach --- perturbation --- biomeasurements --- biodiagnosis --- biomonitoring --- Vitis vinifera L. --- proximal sensing --- precision viticulture --- near infrared --- chemometrics --- non-destructive sensor --- NIRS --- osteopathy --- late preterm --- brain --- splanchnic --- Raman spectroscopy --- hyperspectral imaging --- analytical spectroscopy --- counterfeit and substandard pharmaceuticals --- DFT calculations --- chemometrics --- PLSR --- API --- lumefantrine --- artemether --- antimalarial tablets --- FT-NIR spectroscopy --- PLS-R --- water --- glucose --- test set validation --- RMSEP --- hyperspectral image processing --- perfusion measurements --- clinical classifications --- n/a

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