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Arthropoda. --- Forest canopies. --- Arthropodes --- Couvert forestier --- Arthropoda --- Forest animals --- Forest canopies --- Canopies, Forest --- Canopy cover, Tree --- Cover, Forest crown --- Cover, Forest overstory --- Cover, Tree --- Cover, Tree canopy --- Crown canopies, Forest --- Crown cover, Forest --- Forest crown canopies --- Forest crown cover --- Forest overstory cover --- Overstory cover, Forest --- Tree canopy cover --- Tree cover --- Forests and forestry --- Plant canopies --- Forest fauna --- Woodland animals --- Animals --- Arthropods --- Bilateria --- Invertebrates --- Physiologie

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This volume is devoted to the derivation and application of simplified bioclimatic boundary conditions at vegetated land surfaces using natural selection of vegetation characteristics driven by productivity maximization. It investigates the internal control of forest growth by the vertical fluxes of light, CO2, water vapor, and heat within the canopy, as well as the external control offered by the balances of thermal energy and water. Through these means it seeks to determine how the physical characteristics and productivity of forest communities are related to the climates and soils in which they are found. Ecohydrology bridges the fields of hydrology and ecology and proposes new unifying principles derived from the concept of natural selection. It also has potential application in determining the response of vegetation to slow variations in climate and will provide fascinating reading for graduate-level students and research scientists working in ecohydrology, hydroclimatology, forest ecology, and surface water hydrology.

Forest ecology. --- Forest productivity. --- Forest plants --- Forest microclimatology. --- Forest canopies. --- Ecohydrology. --- Eco-hydrology --- Aquatic ecology --- Hydrology --- Canopies, Forest --- Canopy cover, Tree --- Cover, Forest crown --- Cover, Forest overstory --- Cover, Tree --- Cover, Tree canopy --- Crown canopies, Forest --- Crown cover, Forest --- Forest crown canopies --- Forest crown cover --- Forest overstory cover --- Overstory cover, Forest --- Tree canopy cover --- Tree cover --- Forests and forestry --- Plant canopies --- Climate and forests --- Forests and climate --- Forest ecology --- Forest influences --- Microclimatology --- Vegetation and climate --- Forest meteorology --- Forest production --- Forest yield --- Productivity, Forest --- Agricultural productivity --- Primary productivity (Biology) --- Forest site quality --- Forest ecosystems --- Ecology --- Forest botany --- Forest flora --- Forest vegetation --- Forest wildlife plants --- Forest-zone plants --- Wildlife plants, Forest --- Woodland plants --- Woodland vegetation --- Plants --- Woodland garden plants --- Ecophysiology. --- Environmental Sciences and Forestry. Forestry --- Forest Ecology.

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Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- artificial neural network (ANN) --- Grain weevil identification --- neural modelling classification --- winter wheat --- grain --- artificial neural network --- ferulic acid --- deoxynivalenol --- nivalenol --- MLP network --- sensitivity analysis --- precision agriculture --- machine learning --- similarity --- metric --- memory --- deep learning --- plant growth --- dynamic response --- root zone temperature --- dynamic model --- NARX neural networks --- hydroponics --- vegetation indices --- UAV --- neural network --- corn plant density --- corn canopy cover --- yield prediction --- CLQ --- GA-BPNN --- GPP-driven spectral model --- rice phenology --- EBK --- correlation filter --- crop yield prediction --- hybrid feature extraction --- recursive feature elimination wrapper --- artificial neural networks --- big data --- classification --- high-throughput phenotyping --- modeling --- predicting --- time series forecasting --- soybean --- food production --- paddy rice mapping --- dynamic time warping --- LSTM --- weakly supervised learning --- cropland mapping --- apparent soil electrical conductivity (ECa) --- magnetic susceptibility (MS) --- EM38 --- neural networks --- Phoenix dactylifera L. --- Medjool dates --- image classification --- convolutional neural networks --- transfer learning --- average degree of coverage --- coverage unevenness coefficient --- optimization --- high-resolution imagery --- oil palm tree --- CNN --- Faster-RCNN --- image identification --- agroecology --- weeds --- yield gap --- environment --- health --- crop models --- soil and plant nutrition --- automated harvesting --- model application for sustainable agriculture --- remote sensing for agriculture --- decision supporting systems --- neural image analysis --- artificial neural network (ANN) --- Grain weevil identification --- neural modelling classification --- winter wheat --- grain --- artificial neural network --- ferulic acid --- deoxynivalenol --- nivalenol --- MLP network --- sensitivity analysis --- precision agriculture --- machine learning --- similarity --- metric --- memory --- deep learning --- plant growth --- dynamic response --- root zone temperature --- dynamic model --- NARX neural networks --- hydroponics --- vegetation indices --- UAV --- neural network --- corn plant density --- corn canopy cover --- yield prediction --- CLQ --- GA-BPNN --- GPP-driven spectral model --- rice phenology --- EBK --- correlation filter --- crop yield prediction --- hybrid feature extraction --- recursive feature elimination wrapper --- artificial neural networks --- big data --- classification --- high-throughput phenotyping --- modeling --- predicting --- time series forecasting --- soybean --- food production --- paddy rice mapping --- dynamic time warping --- LSTM --- weakly supervised learning --- cropland mapping --- apparent soil electrical conductivity (ECa) --- magnetic susceptibility (MS) --- EM38 --- neural networks --- Phoenix dactylifera L. --- Medjool dates --- image classification --- convolutional neural networks --- transfer learning --- average degree of coverage --- coverage unevenness coefficient --- optimization --- high-resolution imagery --- oil palm tree --- CNN --- Faster-RCNN --- image identification --- agroecology --- weeds --- yield gap --- environment --- health --- crop models --- soil and plant nutrition --- automated harvesting --- model application for sustainable agriculture --- remote sensing for agriculture --- decision supporting systems --- neural image analysis

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Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible.

artificial neural network (ANN) --- Grain weevil identification --- neural modelling classification --- winter wheat --- grain --- artificial neural network --- ferulic acid --- deoxynivalenol --- nivalenol --- MLP network --- sensitivity analysis --- precision agriculture --- machine learning --- similarity --- metric --- memory --- deep learning --- plant growth --- dynamic response --- root zone temperature --- dynamic model --- NARX neural networks --- hydroponics --- vegetation indices --- UAV --- neural network --- corn plant density --- corn canopy cover --- yield prediction --- CLQ --- GA-BPNN --- GPP-driven spectral model --- rice phenology --- EBK --- correlation filter --- crop yield prediction --- hybrid feature extraction --- recursive feature elimination wrapper --- artificial neural networks --- big data --- classification --- high-throughput phenotyping --- modeling --- predicting --- time series forecasting --- soybean --- food production --- paddy rice mapping --- dynamic time warping --- LSTM --- weakly supervised learning --- cropland mapping --- apparent soil electrical conductivity (ECa) --- magnetic susceptibility (MS) --- EM38 --- neural networks --- Phoenix dactylifera L. --- Medjool dates --- image classification --- convolutional neural networks --- transfer learning --- average degree of coverage --- coverage unevenness coefficient --- optimization --- high-resolution imagery --- oil palm tree --- CNN --- Faster-RCNN --- image identification --- agroecology --- weeds --- yield gap --- environment --- health --- crop models --- soil and plant nutrition --- automated harvesting --- model application for sustainable agriculture --- remote sensing for agriculture --- decision supporting systems --- neural image analysis

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This book compiles a set of original and innovative papers included in the Special Issue on UAVs for vegetation monitoring, which proves the wide scope of UAVs in very diverse vegetation applications, both in agricultural and forestry scenarios, ranging from the characterization of relevant vegetation features to the detection of plant or crop stressors. New methods and techniques are developed and applied to diverse vegetation scenarios to meet the main challenge of sustainability.

Research & information: general --- UAS --- UAV --- vegetation cover --- multispectral --- land cover --- forest --- Acacia --- Indonesia --- tropics --- vegetation ground cover --- vegetation indices --- agro-environmental measures --- olive groves --- southern Spain --- sUAS --- water stress --- ornamental --- container-grown --- artificial intelligence --- machine learning --- deep learning --- neural network --- visual recognition --- precision agriculture --- canopy cover --- image analysis --- crop mapping --- evapotranspiration (ET) --- GRAPEX --- remote sensing --- Two Source Energy Balance model (TSEB) --- contextual spatial domain/resolution --- data aggregation --- eddy covariance (EC) --- Fusarium wilt --- crop disease --- banana --- multispectral remote sensing --- purple rapeseed leaves --- unmanned aerial vehicle --- U-Net --- plant segmentation --- nitrogen stress --- Glycine max --- RGB --- canopy height --- close remote sensing --- growth model --- curve fitting --- NDVI --- solar zenith angle --- flight altitude --- time of day --- operating parameters --- CNN --- Faster RCNN --- SSD --- Inception v2 --- patch-based CNN --- MobileNet v2 --- detection performance --- inference time --- disease detection --- cotton root rot --- plant-level --- single-plant --- plant-by-plant --- classification --- UAV remote sensing --- crop monitoring --- RGB imagery --- multispectral imagery --- century-old biochar --- semantic segmentation --- random forest --- crop canopy --- multispectral image --- chlorophyll content --- remote sensing technique --- individual plant segmentation --- plant detection --- transfer learning --- maize tassel --- tassel branch number --- convolution neural network --- VGG16 --- plant nitrogen estimation --- vegetation index --- image segmentation --- transpiration --- method comparison --- oil palm --- multiple linear regression --- support vector machine --- artificial neural network --- UAV hyperspectral --- wheat yellow rust --- disease monitoring --- texture --- spatial resolution --- RGB camera --- thermal camera --- drought tolerance --- forage grass --- HSV --- CIELab --- broad-sense heritability --- phenotyping gap --- high throughput field phenotyping --- UAV digital images --- winter wheat biomass --- multiscale textures --- red-edge spectra --- least squares support vector machine --- variable importance --- drone --- hyperspectral --- thermal --- nutrient deficiency --- weed detection --- disease diagnosis --- plant trails