UAVs for Vegetation Monitoring (MDPI - Multidisciplinary Digital Publishing Institute, 2021)
Keywords: Acacia; agro-environmental measures; artificial intelligence; artificial neural network; banana; broad-sense heritability; canopy cover; canopy height; century-old biochar; chlorophyll content; CIELab; classification; close remote sensing; CNN; container-grown; contextual spatial domain/resolution; convolution neural network; cotton root rot; crop canopy; crop disease; crop mapping; crop monitoring; curve fitting; data aggregation; deep learning; detection performance; disease detection; disease diagnosis; disease monitoring; drone; drought tolerance; eddy covariance (EC); evapotranspiration (ET); Faster RCNN; flight altitude; forage grass; forest; Fusarium wilt; Glycine max; GRAPEX; growth model; high throughput field phenotyping; HSV; hyperspectral; image analysis; image segmentation; Inception v2; individual plant segmentation; Indonesia; inference time; land cover; least squares support vector machine; machine learning; maize tassel; method comparison; MobileNet v2; multiple linear regression; multiscale textures; multispectral; multispectral image; multispectral imagery; multispectral remote sensing; NDVI; neural network; nitrogen stress; nutrient deficiency; oil palm; olive groves; operating parameters; ornamental; patch-based CNN; phenotyping gap; plant detection; plant nitrogen estimation; plant segmentation; plant trails; plant-by-plant; plant-level; precision agriculture; purple rapeseed leaves; random forest; red-edge spectra; Reference, Information and Interdisciplinary subjects; remote sensing; remote sensing technique; Research and information: general; RGB; RGB camera; RGB imagery; semantic segmentation; single-plant; solar zenith angle; southern Spain; spatial resolution; SSD; sUAS; support vector machine; tassel branch number; texture; thermal