Crop Disease Detection Using Remote Sensing Image Analysis (MDPI - Multidisciplinary Digital Publishing Institute, 2022)
Keywords: Akaike information criterion (AIC) method; anthocyanin; band selection; broccoli; canopy model of row crops; canopy structure feature; centernet; deep learning; detect the crop disease; difference index (DI); disease detection; early; Earth Sciences, Geography, Environment, Planning; efficientdet; food security; Geography; hotspot; hyperspectral; hyperspectral imaging; hyperspectral reflectance; image fusion; linear discriminant analysis; lodging; machine learning; maturity detection; meteorological information; multiple scattering for geometric optical approach; multispectral imaging; n/a; NDVI; object detection; outbreak prediction; overlapping relationship; plant disease; polarization; precision agriculture; precision crop protection; proximal sensing; quantify the disease severity; Reference, Information and Interdisciplinary subjects; regional remote sensing; remote sensing; Research and information: general; retinanet; sensor fusion; signal-to-noise ratio; texture; the gap probabilities of row crops; thermal; thermal imaging; UAV; UAV images; unmanned aerial vehicle; unmanned aerial vehicle (UAV); unsupervised clustering; vegetation health monitoring; vegetation indices; wheat powdery mildew; wheat yellow rust