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...
Guardado en:
| Formato: | Online |
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| Lenguaje: | inglés |
| Publicado: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Materias: | |
| Acceso en línea: | ONIX_20221117_9783036556062_120 |
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| _version_ | 1869531257838239744 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-93863 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-938632024-03-28T03:31:37Z Crop Disease Detection Using Remote Sensing Image Analysis Pantazi, Xanthoula Eirini 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography 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. 2022-11-17T16:28:21Z 2022-11-17T16:28:21Z 2022 book ONIX_20221117_9783036556062_120 9783036556062 9783036556055 https://directory.doabooks.org/handle/20.500.12854/93863 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/6293 https://mdpi.com/books/pdfview/book/6293 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-5606-2 10.3390/books978-3-0365-5606-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036556062 9783036556055 202 Basel open access |
| spellingShingle | 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography Crop Disease Detection Using Remote Sensing Image Analysis |
| title | Crop Disease Detection Using Remote Sensing Image Analysis |
| title_full | Crop Disease Detection Using Remote Sensing Image Analysis |
| title_fullStr | Crop Disease Detection Using Remote Sensing Image Analysis |
| title_full_unstemmed | Crop Disease Detection Using Remote Sensing Image Analysis |
| title_short | Crop Disease Detection Using Remote Sensing Image Analysis |
| title_sort | crop disease detection using remote sensing image analysis |
| topic | 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography |
| topic_facet | 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 thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography |
| url | ONIX_20221117_9783036556062_120 |