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Crop Disease Detection Using Remote Sensing Image Analysis

Resumen/Descripción – provisto por la editorial

No disponible.

Palabras clave – provistas por la editorial

hyperspectral; thermal; proximal sensing; disease detection; signal-to-noise ratio; outbreak prediction; sensor fusion; unsupervised clustering; multispectral imaging; thermal imaging; unmanned aerial vehicle; UAV; lodging; unmanned aerial vehicle (UAV); canopy structure feature; Akaike information criterion (AIC) method; difference index (DI); texture; canopy model of row crops; multiple scattering for geometric optical approach; the gap probabilities of row crops; overlapping relationship; hotspot; n/a; wheat yellow rust; vegetation indices; meteorological information; food security; regional remote sensing; vegetation health monitoring; remote sensing; NDVI; polarization; image fusion; wheat powdery mildew; hyperspectral imaging; early; detect the crop disease; quantify the disease severity; plant disease; band selection; machine learning; anthocyanin; hyperspectral reflectance; linear discriminant analysis; precision crop protection; object detection; UAV images; maturity detection; efficientdet; retinanet; centernet; deep learning; precision agriculture; broccoli

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Información

Tipo de recurso:

libros

ISBN electrónico

978-3-0365-5606-2

País de edición

Suiza