Remote sensing monitoring of wheat leaf rust based on UAV multispectral imagery and the BPNN method
Abstract Wheat (Triticum aestivum L.) leaf rust is the most common and widely distributed wheat disease. Non‐destructive and real‐time methods for monitoring wheat leaf rust can help prevent and control plant diseases in agricultural production. In this study, we obtained multispectral imagery of th...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-07-01
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| Series: | Food and Energy Security |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/fes3.477 |
| _version_ | 1827897699788128256 |
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| author | Chengxin Ju Chen Chen Rui Li Yuanyuan Zhao Xiaochun Zhong Ruilin Sun Tao Liu Chengming Sun |
| author_facet | Chengxin Ju Chen Chen Rui Li Yuanyuan Zhao Xiaochun Zhong Ruilin Sun Tao Liu Chengming Sun |
| author_sort | Chengxin Ju |
| collection | DOAJ |
| description | Abstract Wheat (Triticum aestivum L.) leaf rust is the most common and widely distributed wheat disease. Non‐destructive and real‐time methods for monitoring wheat leaf rust can help prevent and control plant diseases in agricultural production. In this study, we obtained multispectral imagery of the wheat canopy acquired by an unmanned aerial vehicle, selected the vegetation index using the K‐means algorithm (KA) and genetic algorithm (GA), and established a wheat leaf rust monitoring model based on the backpropagation neural network (BPNN) method. The results showed that the R2 and RMSE of the KA‐BPNN model were 0.902% and 5.45% for the modeling set, respectively, and 0.784% and 4.76% for the validation set, respectively; and the R2 and RMSE of the GA‐BPNN model was 0.922% and 4.88% for the modeling set, respectively, and 0.780% and 4.28% for the validation set, respectively. The prediction model after optimizing the variables using KA and GA had higher accuracy than the BPNN model, implying that using variable dimensionality reduction methods and complex machine learning algorithms to construct estimation models can improve model accuracy significantly. These models accurately monitored leaf rust in winter wheat, providing a theoretical basis and technical support for assessing plant diseases and screening disease‐resistant wheat varieties. |
| first_indexed | 2024-03-12T22:53:23Z |
| format | Article |
| id | doaj.art-c7431fad1e2848c6bf50637e28a54ed0 |
| institution | Directory Open Access Journal |
| issn | 2048-3694 |
| language | English |
| last_indexed | 2024-03-12T22:53:23Z |
| publishDate | 2023-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Food and Energy Security |
| spelling | doaj.art-c7431fad1e2848c6bf50637e28a54ed02023-07-20T06:37:10ZengWileyFood and Energy Security2048-36942023-07-01124n/an/a10.1002/fes3.477Remote sensing monitoring of wheat leaf rust based on UAV multispectral imagery and the BPNN methodChengxin Ju0Chen Chen1Rui Li2Yuanyuan Zhao3Xiaochun Zhong4Ruilin Sun5Tao Liu6Chengming Sun7Jiangsu Key Laboratory of Crop Genetics and Physiology/Co‐Innovation Center for Modern Production Technology of Grain Crops Yangzhou University Yangzhou ChinaZhenjiang Agricultural Science Research Institute of Jiangsu Hilly Area Jurong ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Co‐Innovation Center for Modern Production Technology of Grain Crops Yangzhou University Yangzhou ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Co‐Innovation Center for Modern Production Technology of Grain Crops Yangzhou University Yangzhou ChinaAgricultural Information Institute Chinese Academy of Agricultural Sciences Beijing ChinaAgricultural Mechanization Technology Promotion Service Station Guannan ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Co‐Innovation Center for Modern Production Technology of Grain Crops Yangzhou University Yangzhou ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Co‐Innovation Center for Modern Production Technology of Grain Crops Yangzhou University Yangzhou ChinaAbstract Wheat (Triticum aestivum L.) leaf rust is the most common and widely distributed wheat disease. Non‐destructive and real‐time methods for monitoring wheat leaf rust can help prevent and control plant diseases in agricultural production. In this study, we obtained multispectral imagery of the wheat canopy acquired by an unmanned aerial vehicle, selected the vegetation index using the K‐means algorithm (KA) and genetic algorithm (GA), and established a wheat leaf rust monitoring model based on the backpropagation neural network (BPNN) method. The results showed that the R2 and RMSE of the KA‐BPNN model were 0.902% and 5.45% for the modeling set, respectively, and 0.784% and 4.76% for the validation set, respectively; and the R2 and RMSE of the GA‐BPNN model was 0.922% and 4.88% for the modeling set, respectively, and 0.780% and 4.28% for the validation set, respectively. The prediction model after optimizing the variables using KA and GA had higher accuracy than the BPNN model, implying that using variable dimensionality reduction methods and complex machine learning algorithms to construct estimation models can improve model accuracy significantly. These models accurately monitored leaf rust in winter wheat, providing a theoretical basis and technical support for assessing plant diseases and screening disease‐resistant wheat varieties.https://doi.org/10.1002/fes3.477backpropagation neural networkmultispectral imageryspectral reflectancevegetation indexwheat leaf rust |
| spellingShingle | Chengxin Ju Chen Chen Rui Li Yuanyuan Zhao Xiaochun Zhong Ruilin Sun Tao Liu Chengming Sun Remote sensing monitoring of wheat leaf rust based on UAV multispectral imagery and the BPNN method Food and Energy Security backpropagation neural network multispectral imagery spectral reflectance vegetation index wheat leaf rust |
| title | Remote sensing monitoring of wheat leaf rust based on UAV multispectral imagery and the BPNN method |
| title_full | Remote sensing monitoring of wheat leaf rust based on UAV multispectral imagery and the BPNN method |
| title_fullStr | Remote sensing monitoring of wheat leaf rust based on UAV multispectral imagery and the BPNN method |
| title_full_unstemmed | Remote sensing monitoring of wheat leaf rust based on UAV multispectral imagery and the BPNN method |
| title_short | Remote sensing monitoring of wheat leaf rust based on UAV multispectral imagery and the BPNN method |
| title_sort | remote sensing monitoring of wheat leaf rust based on uav multispectral imagery and the bpnn method |
| topic | backpropagation neural network multispectral imagery spectral reflectance vegetation index wheat leaf rust |
| url | https://doi.org/10.1002/fes3.477 |
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