UAVs for Vegetation Monitoring

• Autore: de Castro Megías Ana
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (452 p.)
• Soggetto topico: Research & information: general
• Soggetto non controllato: 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; remote sensing; remote sensing technique; 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; thermal camera; time of day; transfer learning; transpiration; tropics; Two Source Energy Balance model (TSEB); U-Net; UAS; UAV; UAV digital images; UAV hyperspectral; UAV remote sensing; unmanned aerial vehicle; variable importance; vegetation cover; vegetation ground cover; vegetation index; vegetation indices; VGG16; visual recognition; water stress; weed detection; wheat yellow rust; winter wheat biomass
• Persona (resp. second.): ShiYeyin; PeñaJose M; MajaJoe; de Castro MegíasAna
• Sommario/riassunto: 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.
• Titolo autorizzato: UAVs for Vegetation Monitoring
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557661103321
• Lo trovi qui: Univ. Federico II