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Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology

Sclerotinia sclerotiorum, one of the major diseases infecting oilseed rape leaves, has seriously affected crop yield and quality. In this study, an indoor unmanned aerial vehicle (UAV) low-altitude remote sensing simulation platform was built for disease detection. Thermal, multispectral and RGB ima...

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Autores principales: Cao, Feng, Liu, Fei, Guo, Han, Kong, Wenwen, Zhang, Chu, He, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308689/
https://www.ncbi.nlm.nih.gov/pubmed/30562959
http://dx.doi.org/10.3390/s18124464
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author Cao, Feng
Liu, Fei
Guo, Han
Kong, Wenwen
Zhang, Chu
He, Yong
author_facet Cao, Feng
Liu, Fei
Guo, Han
Kong, Wenwen
Zhang, Chu
He, Yong
author_sort Cao, Feng
collection PubMed
description Sclerotinia sclerotiorum, one of the major diseases infecting oilseed rape leaves, has seriously affected crop yield and quality. In this study, an indoor unmanned aerial vehicle (UAV) low-altitude remote sensing simulation platform was built for disease detection. Thermal, multispectral and RGB images were acquired before and after being artificially inoculated with Sclerotinia sclerotiorum on oilseed rape leaves. New image registration and fusion methods based on scale-invariant feature transform (SIFT) were presented to construct a fused database using multi-model images. The changes of temperature distribution in different sections of infected areas were analyzed by processing thermal images, the maximum temperature difference (MTD) on a single leaf reached 1.7 degrees Celsius 24 h after infection. Four machine learning models were established using thermal images and fused images respectively, including support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN) and naïve Bayes (NB). The results demonstrated that the classification accuracy was improved by 11.3% after image fusion, and the SVM model obtained a classification accuracy of 90.0% on the task of classifying disease severity. The overall results indicated the UAV low-altitude remote sensing simulation platform equipped with multi-sensors could be used to early detect Sclerotinia sclerotiorum on oilseed rape leaves.
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spelling pubmed-63086892019-01-04 Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology Cao, Feng Liu, Fei Guo, Han Kong, Wenwen Zhang, Chu He, Yong Sensors (Basel) Article Sclerotinia sclerotiorum, one of the major diseases infecting oilseed rape leaves, has seriously affected crop yield and quality. In this study, an indoor unmanned aerial vehicle (UAV) low-altitude remote sensing simulation platform was built for disease detection. Thermal, multispectral and RGB images were acquired before and after being artificially inoculated with Sclerotinia sclerotiorum on oilseed rape leaves. New image registration and fusion methods based on scale-invariant feature transform (SIFT) were presented to construct a fused database using multi-model images. The changes of temperature distribution in different sections of infected areas were analyzed by processing thermal images, the maximum temperature difference (MTD) on a single leaf reached 1.7 degrees Celsius 24 h after infection. Four machine learning models were established using thermal images and fused images respectively, including support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN) and naïve Bayes (NB). The results demonstrated that the classification accuracy was improved by 11.3% after image fusion, and the SVM model obtained a classification accuracy of 90.0% on the task of classifying disease severity. The overall results indicated the UAV low-altitude remote sensing simulation platform equipped with multi-sensors could be used to early detect Sclerotinia sclerotiorum on oilseed rape leaves. MDPI 2018-12-17 /pmc/articles/PMC6308689/ /pubmed/30562959 http://dx.doi.org/10.3390/s18124464 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cao, Feng
Liu, Fei
Guo, Han
Kong, Wenwen
Zhang, Chu
He, Yong
Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology
title Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology
title_full Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology
title_fullStr Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology
title_full_unstemmed Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology
title_short Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology
title_sort fast detection of sclerotinia sclerotiorum on oilseed rape leaves using low-altitude remote sensing technology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308689/
https://www.ncbi.nlm.nih.gov/pubmed/30562959
http://dx.doi.org/10.3390/s18124464
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