UAVs for Vegetation Monitoring

doi:10.3390/books978-3-0365-2191-6

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Hardback

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Keywords

UAS; UAV; vegetation cover; multispectral; land cover; forest; Acacia; Indonesia; tropics; UAV; vegetation ground cover; multispectral; 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; precision agriculture; canopy cover; UAS; image analysis; multispectral; crop mapping; evapotranspiration (ET); GRAPEX; sUAS; 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; UAV; purple rapeseed leaves; unmanned aerial vehicle; U-Net; plant segmentation; nitrogen stress; Glycine max; RGB; canopy cover; canopy height; close remote sensing; growth model; curve fitting; NDVI; solar zenith angle; flight altitude; time of day; operating parameters; UAV; CNN; Faster RCNN; SSD; Inception v2; patch-based CNN; MobileNet v2; detection performance; inference time; precision agriculture; UAV; disease detection; cotton root rot; plant-level; single-plant; plant-by-plant; classification; image analysis; machine learning; UAV remote sensing; crop monitoring; RGB imagery; multispectral imagery; century-old biochar; semantic segmentation; machine learning; random forest; deep learning; CNN; UAV; crop canopy; multispectral image; chlorophyll content; remote sensing technique; UAV; crop mapping; image analysis; precision agriculture; deep learning; individual plant segmentation; plant detection; transfer learning; maize tassel; tassel branch number; unmanned aerial vehicle; convolution neural network; VGG16; random forest; UAV; machine learning; plant nitrogen estimation; multispectral imagery; vegetation index; image segmentation; transpiration; method comparison; UAV; oil palm; multiple linear regression; support vector machine; random forest; artificial neural network; UAV hyperspectral; wheat yellow rust; disease monitoring; vegetation index; texture; spatial resolution; UAV; 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; RGB; multispectral; hyperspectral; thermal; machine learning; water stress; nutrient deficiency; weed detection; disease diagnosis; plant trails

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UAVs for Vegetation Monitoring

Edited by

November 2021

466 pages

ISBN978-3-0365-2192-3 (Hardback)
ISBN978-3-0365-2191-6 (PDF)

https://doi.org/10.3390/books978-3-0365-2191-6

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This is a Reprint of the Special Issue UAVs for Vegetation Monitoring that was published in

Engineering

Environmental & Earth Sciences

Summary

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.

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