Crop Disease Detection Using Remote Sensing Image Analysis

• Otros Autores: Pantazi, Xanthoula Eirini (Editor)
• Formato: Electrónico Capítulo de libro
• Lenguaje: inglés
• Publicado: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Materias: Research & information: general; Geography; 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
• Acceso en línea: DOAB: download the publication; DOAB: description of the publication

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease manag...