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Palabras clave – provistas por la editorial

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