Crop Disease Detection Using Remote Sensing Image Analysis

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...

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Bibliographic Details
Other Authors: Pantazi, Xanthoula Eirini (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
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DOAB: description of the publication
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245 1 0 |a Crop Disease Detection Using Remote Sensing Image Analysis 
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520 |a 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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546 |a English 
650 7 |a Research & information: general  |2 bicssc 
650 7 |a Geography  |2 bicssc 
653 |a hyperspectral 
653 |a thermal 
653 |a proximal sensing 
653 |a disease detection 
653 |a signal-to-noise ratio 
653 |a outbreak prediction 
653 |a sensor fusion 
653 |a unsupervised clustering 
653 |a multispectral imaging 
653 |a thermal imaging 
653 |a unmanned aerial vehicle 
653 |a UAV 
653 |a lodging 
653 |a unmanned aerial vehicle (UAV) 
653 |a canopy structure feature 
653 |a Akaike information criterion (AIC) method 
653 |a difference index (DI) 
653 |a texture 
653 |a canopy model of row crops 
653 |a multiple scattering for geometric optical approach 
653 |a the gap probabilities of row crops 
653 |a overlapping relationship 
653 |a hotspot 
653 |a n/a 
653 |a wheat yellow rust 
653 |a vegetation indices 
653 |a meteorological information 
653 |a food security 
653 |a regional remote sensing 
653 |a vegetation health monitoring 
653 |a remote sensing 
653 |a NDVI 
653 |a polarization 
653 |a image fusion 
653 |a wheat powdery mildew 
653 |a hyperspectral imaging 
653 |a early 
653 |a detect the crop disease 
653 |a quantify the disease severity 
653 |a plant disease 
653 |a band selection 
653 |a machine learning 
653 |a anthocyanin 
653 |a hyperspectral reflectance 
653 |a linear discriminant analysis 
653 |a precision crop protection 
653 |a object detection 
653 |a UAV images 
653 |a maturity detection 
653 |a efficientdet 
653 |a retinanet 
653 |a centernet 
653 |a deep learning 
653 |a precision agriculture 
653 |a broccoli 
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856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/93863  |7 0  |z DOAB: description of the publication