UAVs for Vegetation Monitoring

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

dc.contributor.editor	de Castro Megías, Ana
dc.contributor.editor	Shi, Yeyin
dc.contributor.editor	Peña, Jose M.
dc.contributor.editor	Maja, Joe
dc.date.accessioned	2022-01-11T13:47:00Z
dc.date.available	2022-01-11T13:47:00Z
dc.date.issued	2021
dc.identifier	ONIX_20220111_9783036521923_768
dc.identifier.uri	https://directory.doabooks.org/handle/20.500.12854/76936
dc.description.abstract	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.
dc.language	English
dc.subject.classification	thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general	en_US
dc.subject.other	UAS
dc.subject.other	UAV
dc.subject.other	vegetation cover
dc.subject.other	multispectral
dc.subject.other	land cover
dc.subject.other	forest
dc.subject.other	Acacia
dc.subject.other	Indonesia
dc.subject.other	tropics
dc.subject.other	vegetation ground cover
dc.subject.other	vegetation indices
dc.subject.other	agro-environmental measures
dc.subject.other	olive groves
dc.subject.other	southern Spain
dc.subject.other	sUAS
dc.subject.other	water stress
dc.subject.other	ornamental
dc.subject.other	container-grown
dc.subject.other	artificial intelligence
dc.subject.other	machine learning
dc.subject.other	deep learning
dc.subject.other	neural network
dc.subject.other	visual recognition
dc.subject.other	precision agriculture
dc.subject.other	canopy cover
dc.subject.other	image analysis
dc.subject.other	crop mapping
dc.subject.other	evapotranspiration (ET)
dc.subject.other	GRAPEX
dc.subject.other	remote sensing
dc.subject.other	Two Source Energy Balance model (TSEB)
dc.subject.other	contextual spatial domain/resolution
dc.subject.other	data aggregation
dc.subject.other	eddy covariance (EC)
dc.subject.other	Fusarium wilt
dc.subject.other	crop disease
dc.subject.other	banana
dc.subject.other	multispectral remote sensing
dc.subject.other	purple rapeseed leaves
dc.subject.other	unmanned aerial vehicle
dc.subject.other	U-Net
dc.subject.other	plant segmentation
dc.subject.other	nitrogen stress
dc.subject.other	Glycine max
dc.subject.other	RGB
dc.subject.other	canopy height
dc.subject.other	close remote sensing
dc.subject.other	growth model
dc.subject.other	curve fitting
dc.subject.other	NDVI
dc.subject.other	solar zenith angle
dc.subject.other	flight altitude
dc.subject.other	time of day
dc.subject.other	operating parameters
dc.subject.other	CNN
dc.subject.other	Faster RCNN
dc.subject.other	SSD
dc.subject.other	Inception v2
dc.subject.other	patch-based CNN
dc.subject.other	MobileNet v2
dc.subject.other	detection performance
dc.subject.other	inference time
dc.subject.other	disease detection
dc.subject.other	cotton root rot
dc.subject.other	plant-level
dc.subject.other	single-plant
dc.subject.other	plant-by-plant
dc.subject.other	classification
dc.subject.other	UAV remote sensing
dc.subject.other	crop monitoring
dc.subject.other	RGB imagery
dc.subject.other	multispectral imagery
dc.subject.other	century-old biochar
dc.subject.other	semantic segmentation
dc.subject.other	random forest
dc.subject.other	crop canopy
dc.subject.other	multispectral image
dc.subject.other	chlorophyll content
dc.subject.other	remote sensing technique
dc.subject.other	individual plant segmentation
dc.subject.other	plant detection
dc.subject.other	transfer learning
dc.subject.other	maize tassel
dc.subject.other	tassel branch number
dc.subject.other	convolution neural network
dc.subject.other	VGG16
dc.subject.other	plant nitrogen estimation
dc.subject.other	vegetation index
dc.subject.other	image segmentation
dc.subject.other	transpiration
dc.subject.other	method comparison
dc.subject.other	oil palm
dc.subject.other	multiple linear regression
dc.subject.other	support vector machine
dc.subject.other	artificial neural network
dc.subject.other	UAV hyperspectral
dc.subject.other	wheat yellow rust
dc.subject.other	disease monitoring
dc.subject.other	texture
dc.subject.other	spatial resolution
dc.subject.other	RGB camera
dc.subject.other	thermal camera
dc.subject.other	drought tolerance
dc.subject.other	forage grass
dc.subject.other	HSV
dc.subject.other	CIELab
dc.subject.other	broad-sense heritability
dc.subject.other	phenotyping gap
dc.subject.other	high throughput field phenotyping
dc.subject.other	UAV digital images
dc.subject.other	winter wheat biomass
dc.subject.other	multiscale textures
dc.subject.other	red-edge spectra
dc.subject.other	least squares support vector machine
dc.subject.other	variable importance
dc.subject.other	drone
dc.subject.other	hyperspectral
dc.subject.other	thermal
dc.subject.other	nutrient deficiency
dc.subject.other	weed detection
dc.subject.other	disease diagnosis
dc.subject.other	plant trails
dc.title	UAVs for Vegetation Monitoring
dc.type	book
oapen.identifier.doi	10.3390/books978-3-0365-2191-6
oapen.relation.isPublishedBy	46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn	9783036521923
oapen.relation.isbn	9783036521916
oapen.pages	452
oapen.place.publication	Basel, Switzerland

Files in this item

Files	Size	Format	View
There are no files associated with this item.

This item appears in the following Collection(s)

Imported or submitted locally

Show simple item record

Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/