Fast Detection of <i>Sclerotinia Sclerotiorum</i> on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology
<i>Sclerotinia sclerotiorum</i>, 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, multis...
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MDPI AG
2018-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/12/4464 |
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| author | Feng Cao Fei Liu Han Guo Wenwen Kong Chu Zhang Yong He |
| author_facet | Feng Cao Fei Liu Han Guo Wenwen Kong Chu Zhang Yong He |
| author_sort | Feng Cao |
| collection | DOAJ |
| description | <i>Sclerotinia sclerotiorum</i>, 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 <i>Sclerotinia sclerotiorum</i> 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 <i>Sclerotinia sclerotiorum</i> on oilseed rape leaves. |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:35:51Z |
| publishDate | 2018-12-01 |
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| spelling | doaj.art-71fd19dd8f9a4b28a23798b2a9e44e482022-12-22T04:01:46ZengMDPI AGSensors1424-82202018-12-011812446410.3390/s18124464s18124464Fast Detection of <i>Sclerotinia Sclerotiorum</i> on Oilseed Rape Leaves Using Low-Altitude Remote Sensing TechnologyFeng Cao0Fei Liu1Han Guo2Wenwen Kong3Chu Zhang4Yong He5College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China<i>Sclerotinia sclerotiorum</i>, 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 <i>Sclerotinia sclerotiorum</i> 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 <i>Sclerotinia sclerotiorum</i> on oilseed rape leaves.https://www.mdpi.com/1424-8220/18/12/4464<i>Sclerotinia sclerotiorum</i>oilseed rapemultispectral technologythermal imaging technologyimage fusionmachine learning |
| spellingShingle | Feng Cao Fei Liu Han Guo Wenwen Kong Chu Zhang Yong He Fast Detection of <i>Sclerotinia Sclerotiorum</i> on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology Sensors <i>Sclerotinia sclerotiorum</i> oilseed rape multispectral technology thermal imaging technology image fusion machine learning |
| title | Fast Detection of <i>Sclerotinia Sclerotiorum</i> on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology |
| title_full | Fast Detection of <i>Sclerotinia Sclerotiorum</i> on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology |
| title_fullStr | Fast Detection of <i>Sclerotinia Sclerotiorum</i> on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology |
| title_full_unstemmed | Fast Detection of <i>Sclerotinia Sclerotiorum</i> on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology |
| title_short | Fast Detection of <i>Sclerotinia Sclerotiorum</i> on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology |
| title_sort | fast detection of i sclerotinia sclerotiorum i on oilseed rape leaves using low altitude remote sensing technology |
| topic | <i>Sclerotinia sclerotiorum</i> oilseed rape multispectral technology thermal imaging technology image fusion machine learning |
| url | https://www.mdpi.com/1424-8220/18/12/4464 |
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