Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery
Crop disease identification and monitoring is an important research topic in smart agriculture. In particular, it is a prerequisite for disease detection and the mapping of infected areas. Wheat fusarium head blight (FHB) is a serious threat to the quality and yield of wheat, so the rapid monitoring...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/2/293 |
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| author | Chunfeng Gao Xingjie Ji Qiang He Zheng Gong Heguang Sun Tiantian Wen Wei Guo |
| author_facet | Chunfeng Gao Xingjie Ji Qiang He Zheng Gong Heguang Sun Tiantian Wen Wei Guo |
| author_sort | Chunfeng Gao |
| collection | DOAJ |
| description | Crop disease identification and monitoring is an important research topic in smart agriculture. In particular, it is a prerequisite for disease detection and the mapping of infected areas. Wheat fusarium head blight (FHB) is a serious threat to the quality and yield of wheat, so the rapid monitoring of wheat FHB is important. This study proposed a method based on unmanned aerial vehicle (UAV) low-altitude remote sensing and multispectral imaging technology combined with spectral and textural analysis to monitor FHB. First, the multispectral imagery of the wheat population was collected by UAV. Second, 10 vegetation indices (VIs)were extracted from multispectral imagery. In addition, three types of textural indices (TIs), including the normalized difference texture index (NDTI), difference texture index (DTI), and ratio texture index (RTI) were extracted for subsequent analysis and modeling. Finally, VIs, TIs, and VIs and TIs integrated as the input features, combined with k-nearest neighbor (KNN), the particle swarm optimization support vector machine (PSO-SVM), and XGBoost were used to construct wheat FHB monitoring models. The results showed that the XGBoost algorithm with the fusion of VIs and TIs as the input features has the highest performance with the accuracy and F1 score of the test set being 93.63% and 92.93%, respectively. This study provides a new approach and technology for the rapid and nondestructive monitoring of wheat FHB. |
| first_indexed | 2024-03-11T09:19:07Z |
| format | Article |
| id | doaj.art-97da017c9a6940008122daa4356efa5e |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T09:19:07Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-97da017c9a6940008122daa4356efa5e2023-11-16T18:29:01ZengMDPI AGAgriculture2077-04722023-01-0113229310.3390/agriculture13020293Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral ImageryChunfeng Gao0Xingjie Ji1Qiang He2Zheng Gong3Heguang Sun4Tiantian Wen5Wei Guo6College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, ChinaHenan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou 450003, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, ChinaCrop disease identification and monitoring is an important research topic in smart agriculture. In particular, it is a prerequisite for disease detection and the mapping of infected areas. Wheat fusarium head blight (FHB) is a serious threat to the quality and yield of wheat, so the rapid monitoring of wheat FHB is important. This study proposed a method based on unmanned aerial vehicle (UAV) low-altitude remote sensing and multispectral imaging technology combined with spectral and textural analysis to monitor FHB. First, the multispectral imagery of the wheat population was collected by UAV. Second, 10 vegetation indices (VIs)were extracted from multispectral imagery. In addition, three types of textural indices (TIs), including the normalized difference texture index (NDTI), difference texture index (DTI), and ratio texture index (RTI) were extracted for subsequent analysis and modeling. Finally, VIs, TIs, and VIs and TIs integrated as the input features, combined with k-nearest neighbor (KNN), the particle swarm optimization support vector machine (PSO-SVM), and XGBoost were used to construct wheat FHB monitoring models. The results showed that the XGBoost algorithm with the fusion of VIs and TIs as the input features has the highest performance with the accuracy and F1 score of the test set being 93.63% and 92.93%, respectively. This study provides a new approach and technology for the rapid and nondestructive monitoring of wheat FHB.https://www.mdpi.com/2077-0472/13/2/293unmanned aerial vehiclemultispectral imageryfusarium head blighttexture indicesmachine learning |
| spellingShingle | Chunfeng Gao Xingjie Ji Qiang He Zheng Gong Heguang Sun Tiantian Wen Wei Guo Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery Agriculture unmanned aerial vehicle multispectral imagery fusarium head blight texture indices machine learning |
| title | Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery |
| title_full | Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery |
| title_fullStr | Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery |
| title_full_unstemmed | Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery |
| title_short | Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery |
| title_sort | monitoring of wheat fusarium head blight on spectral and textural analysis of uav multispectral imagery |
| topic | unmanned aerial vehicle multispectral imagery fusarium head blight texture indices machine learning |
| url | https://www.mdpi.com/2077-0472/13/2/293 |
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