Early Detection of Verticillium Wilt in Cotton by Using Hyperspectral Imaging Combined with Recurrence Plots
Cotton is susceptible to Verticillium wilt (VW) during its growth. Early and accurate detection of VW can facilitate targeted pesticide treatment and reduce the potential spread of the disease. However, accurately detecting VW in cotton before symptoms appear (the asymptomatic period) after infectio...
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MDPI AG
2025-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/213 |
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| author | Fei Tan Xiuwen Gao Hao Cang Nianyi Wu Ruoyu Di Jingkun Yan Chengkai Li Pan Gao Xin Lv |
| author_facet | Fei Tan Xiuwen Gao Hao Cang Nianyi Wu Ruoyu Di Jingkun Yan Chengkai Li Pan Gao Xin Lv |
| author_sort | Fei Tan |
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| description | Cotton is susceptible to Verticillium wilt (VW) during its growth. Early and accurate detection of VW can facilitate targeted pesticide treatment and reduce the potential spread of the disease. However, accurately detecting VW in cotton before symptoms appear (the asymptomatic period) after infection by <i>Verticillium dahliae</i> remains challenging. This study proposes an early detection method for cotton wilt disease using hyperspectral imaging and recurrence plots (RP) combined with machine learning techniques. First, spectral curves were collected and analyzed under three conditions of cotton plants: healthy, asymptomatic, and symptomatic. Then, the one-dimensional spectral curve was transformed into two-dimensional recurrence plots to enhance the detail differences in the original spectral curve of cotton plants in various states. Hyperspectral recurrence plots contain rich texture information; fifteen texture features were extracted from the spectral recurrence plots using the Gray-Level Gradient Co-occurrence Matrix (GLGCM). Eleven of these texture features showed a strong correlation with the class labels of the cotton plants. In order to reduce redundant information between features, principal component analysis (PCA) was used to extract the first five principal components, which explained 99.02% of the information from the 11 features. The final principal component dataset was then input into KNN, SVM, ELM, and XGBoost classifiers to assess the accuracy of early detection of VW in cotton. The results showed that the XGBoost model, based on the first five principal components obtained from the texture features, achieved accuracy, precision, recall, and F1-score of 96.3%, 95.6%, 96%, and 95.8%, demonstrating a high classification capability. The results of this study confirm the feasibility of converting spectral curves into recurrence plots and extracting image texture features for the accurate identification of VW in cotton during the asymptomatic period. This method also provides a new strategy for early disease detection of cotton and other plants in the future. |
| format | Article |
| id | doaj-art-a5bd0e30438a4bb0ac96e9fc2f9f1219 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Agronomy |
| spelling | doaj-art-a5bd0e30438a4bb0ac96e9fc2f9f12192025-01-24T13:17:12ZengMDPI AGAgronomy2073-43952025-01-0115121310.3390/agronomy15010213Early Detection of Verticillium Wilt in Cotton by Using Hyperspectral Imaging Combined with Recurrence PlotsFei Tan0Xiuwen Gao1Hao Cang2Nianyi Wu3Ruoyu Di4Jingkun Yan5Chengkai Li6Pan Gao7Xin Lv8College of Information Science and Technology, Shihezi University, Shihezi 832003, ChinaCollege of Information Science and Technology, Shihezi University, Shihezi 832003, ChinaCollege of Information Science and Technology, Shihezi University, Shihezi 832003, ChinaCollege of Information Science and Technology, Shihezi University, Shihezi 832003, ChinaCollege of Information Science and Technology, Shihezi University, Shihezi 832003, ChinaCollege of Information Science and Technology, Shihezi University, Shihezi 832003, ChinaCollege of Information Science and Technology, Shihezi University, Shihezi 832003, ChinaCollege of Information Science and Technology, Shihezi University, Shihezi 832003, ChinaCollege of Agriculture, Shihezi University, Shihezi 832003, ChinaCotton is susceptible to Verticillium wilt (VW) during its growth. Early and accurate detection of VW can facilitate targeted pesticide treatment and reduce the potential spread of the disease. However, accurately detecting VW in cotton before symptoms appear (the asymptomatic period) after infection by <i>Verticillium dahliae</i> remains challenging. This study proposes an early detection method for cotton wilt disease using hyperspectral imaging and recurrence plots (RP) combined with machine learning techniques. First, spectral curves were collected and analyzed under three conditions of cotton plants: healthy, asymptomatic, and symptomatic. Then, the one-dimensional spectral curve was transformed into two-dimensional recurrence plots to enhance the detail differences in the original spectral curve of cotton plants in various states. Hyperspectral recurrence plots contain rich texture information; fifteen texture features were extracted from the spectral recurrence plots using the Gray-Level Gradient Co-occurrence Matrix (GLGCM). Eleven of these texture features showed a strong correlation with the class labels of the cotton plants. In order to reduce redundant information between features, principal component analysis (PCA) was used to extract the first five principal components, which explained 99.02% of the information from the 11 features. The final principal component dataset was then input into KNN, SVM, ELM, and XGBoost classifiers to assess the accuracy of early detection of VW in cotton. The results showed that the XGBoost model, based on the first five principal components obtained from the texture features, achieved accuracy, precision, recall, and F1-score of 96.3%, 95.6%, 96%, and 95.8%, demonstrating a high classification capability. The results of this study confirm the feasibility of converting spectral curves into recurrence plots and extracting image texture features for the accurate identification of VW in cotton during the asymptomatic period. This method also provides a new strategy for early disease detection of cotton and other plants in the future.https://www.mdpi.com/2073-4395/15/1/213early detectionVerticillium wilthyperspectral imagingrecurrence plot |
| spellingShingle | Fei Tan Xiuwen Gao Hao Cang Nianyi Wu Ruoyu Di Jingkun Yan Chengkai Li Pan Gao Xin Lv Early Detection of Verticillium Wilt in Cotton by Using Hyperspectral Imaging Combined with Recurrence Plots Agronomy early detection Verticillium wilt hyperspectral imaging recurrence plot |
| title | Early Detection of Verticillium Wilt in Cotton by Using Hyperspectral Imaging Combined with Recurrence Plots |
| title_full | Early Detection of Verticillium Wilt in Cotton by Using Hyperspectral Imaging Combined with Recurrence Plots |
| title_fullStr | Early Detection of Verticillium Wilt in Cotton by Using Hyperspectral Imaging Combined with Recurrence Plots |
| title_full_unstemmed | Early Detection of Verticillium Wilt in Cotton by Using Hyperspectral Imaging Combined with Recurrence Plots |
| title_short | Early Detection of Verticillium Wilt in Cotton by Using Hyperspectral Imaging Combined with Recurrence Plots |
| title_sort | early detection of verticillium wilt in cotton by using hyperspectral imaging combined with recurrence plots |
| topic | early detection Verticillium wilt hyperspectral imaging recurrence plot |
| url | https://www.mdpi.com/2073-4395/15/1/213 |
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