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Crop Disease Detection Using Remote Sensing Image Analysis
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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.
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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
- 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 & interdisciplinary subjects
- regional remote sensing
- Remote sensing
- Research & 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
Links
DOI: 10.3390/books978-3-0365-5606-2Editions