UAVs for Vegetation Monitoring

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 vegetati...

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Year of Publication:2021
Language:English
Physical Description:1 electronic resource (452 p.)
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520 |a 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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653 |a UAS 
653 |a UAV 
653 |a vegetation cover 
653 |a multispectral 
653 |a land cover 
653 |a forest 
653 |a Acacia 
653 |a Indonesia 
653 |a tropics 
653 |a vegetation ground cover 
653 |a vegetation indices 
653 |a agro-environmental measures 
653 |a olive groves 
653 |a southern Spain 
653 |a sUAS 
653 |a water stress 
653 |a ornamental 
653 |a container-grown 
653 |a artificial intelligence 
653 |a machine learning 
653 |a deep learning 
653 |a neural network 
653 |a visual recognition 
653 |a precision agriculture 
653 |a canopy cover 
653 |a image analysis 
653 |a crop mapping 
653 |a evapotranspiration (ET) 
653 |a GRAPEX 
653 |a remote sensing 
653 |a Two Source Energy Balance model (TSEB) 
653 |a contextual spatial domain/resolution 
653 |a data aggregation 
653 |a eddy covariance (EC) 
653 |a Fusarium wilt 
653 |a crop disease 
653 |a banana 
653 |a multispectral remote sensing 
653 |a purple rapeseed leaves 
653 |a unmanned aerial vehicle 
653 |a U-Net 
653 |a plant segmentation 
653 |a nitrogen stress 
653 |a Glycine max 
653 |a RGB 
653 |a canopy height 
653 |a close remote sensing 
653 |a growth model 
653 |a curve fitting 
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653 |a solar zenith angle 
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653 |a operating parameters 
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653 |a Faster RCNN 
653 |a SSD 
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653 |a patch-based CNN 
653 |a MobileNet v2 
653 |a detection performance 
653 |a inference time 
653 |a disease detection 
653 |a cotton root rot 
653 |a plant-level 
653 |a single-plant 
653 |a plant-by-plant 
653 |a classification 
653 |a UAV remote sensing 
653 |a crop monitoring 
653 |a RGB imagery 
653 |a multispectral imagery 
653 |a century-old biochar 
653 |a semantic segmentation 
653 |a random forest 
653 |a crop canopy 
653 |a multispectral image 
653 |a chlorophyll content 
653 |a remote sensing technique 
653 |a individual plant segmentation 
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653 |a transfer learning 
653 |a maize tassel 
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653 |a convolution neural network 
653 |a VGG16 
653 |a plant nitrogen estimation 
653 |a vegetation index 
653 |a image segmentation 
653 |a transpiration 
653 |a method comparison 
653 |a oil palm 
653 |a multiple linear regression 
653 |a support vector machine 
653 |a artificial neural network 
653 |a UAV hyperspectral 
653 |a wheat yellow rust 
653 |a disease monitoring 
653 |a texture 
653 |a spatial resolution 
653 |a RGB camera 
653 |a thermal camera 
653 |a drought tolerance 
653 |a forage grass 
653 |a HSV 
653 |a CIELab 
653 |a broad-sense heritability 
653 |a phenotyping gap 
653 |a high throughput field phenotyping 
653 |a UAV digital images 
653 |a winter wheat biomass 
653 |a multiscale textures 
653 |a red-edge spectra 
653 |a least squares support vector machine 
653 |a variable importance 
653 |a drone 
653 |a hyperspectral 
653 |a thermal 
653 |a nutrient deficiency 
653 |a weed detection 
653 |a disease diagnosis 
653 |a plant trails 
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700 1 |a Shi, Yeyin  |4 edt 
700 1 |a Peña, Jose M.  |4 edt 
700 1 |a Maja, Joe  |4 edt 
700 1 |a de Castro Megías, Ana  |4 oth 
700 1 |a Shi, Yeyin  |4 oth 
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