Crop Disease Detection Using Remote Sensing Image Analysis

• Other Authors: Pantazi, Xanthoula Eirini (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: 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
• Online Access: 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 management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops.