UAVs for Vegetation Monitoring

de Castro Megías, Ana ; Shi, Yeyin ; et al.

Basel, Switzerland: MDPI - Multidisciplinary Digital Publishing Institute, 2021

Online E-Book - 452

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View record at Directory of Open Access Books (Volltext)

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.

Titel:	UAVs for Vegetation Monitoring
Autor/in / Beteiligte Person:	de Castro Megías, Ana ; Shi, Yeyin ; Peña, Jose M. ; Maja, Joe
Link:	View record at Directory of Open Access Books (Volltext)
Veröffentlichung:	Basel, Switzerland: MDPI - Multidisciplinary Digital Publishing Institute, 2021
Medientyp:	E-Book
Umfang:	452
ISBN:	978-3-0365-2192-3 (print) ; 978-3-0365-2191-6 (print)
DOI:	10.3390/books978-3-0365-2191-6
Schlagwort:	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 thema EDItEUR:G Reference, Information and Interdisciplinary subjects:GP Research and information: general
Sonstiges:	Nachgewiesen in: Directory of Open Access Books Sprachen: English Document Type: eBook File Description: image/jpeg Language: English Rights: Attribution 4.0 International ; URL: https://creativecommons.org/licenses/by/4.0/ Notes: ONIX_20220111_9783036521923_768

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