Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine Learning
Peanut southern blight has a severe impact on peanut production and is one of the most devastating soil-borne fungal diseases. We conducted a hyperspectral analysis of the spectral responses of plants to peanut southern blight to provide theoretical support for detecting the severity of the disease...
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MDPI AG
2023-07-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/8/1504 |
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| author | Wei Guo Heguang Sun Hongbo Qiao Hui Zhang Lin Zhou Ping Dong Xiaoyu Song |
| author_facet | Wei Guo Heguang Sun Hongbo Qiao Hui Zhang Lin Zhou Ping Dong Xiaoyu Song |
| author_sort | Wei Guo |
| collection | DOAJ |
| description | Peanut southern blight has a severe impact on peanut production and is one of the most devastating soil-borne fungal diseases. We conducted a hyperspectral analysis of the spectral responses of plants to peanut southern blight to provide theoretical support for detecting the severity of the disease via remote sensing. In this study, we collected leaf-level spectral data during the winter of 2021 and the spring of 2022 in a greenhouse laboratory. We explored the spectral response mechanisms of diseased peanut leaves and developed a method for assessing the severity of peanut southern blight disease by comparing the continuous wavelet transform (CWT) with traditional spectral indices and incorporating machine learning techniques. The results showed that the SVM model performed best and was able to effectively detect the severity of peanut southern blight when using CWT (WF<sub>770~780</sub>, 5) as an input feature. The overall accuracy (OA) of the modeling dataset was 91.8% and the kappa coefficient was 0.88. For the validation dataset, the OA was 90.5% and the kappa coefficient was 0.87. These findings highlight the potential of this CWT-based method for accurately assessing the severity of peanut southern blight. |
| first_indexed | 2024-03-11T00:12:25Z |
| format | Article |
| id | doaj.art-5a207649af1a41afb769fbcc82e7ed35 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T00:12:25Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-5a207649af1a41afb769fbcc82e7ed352023-11-18T23:51:01ZengMDPI AGAgriculture2077-04722023-07-01138150410.3390/agriculture13081504Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine LearningWei Guo0Heguang Sun1Hongbo Qiao2Hui Zhang3Lin Zhou4Ping Dong5Xiaoyu Song6College 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 Plant Protection, Henan Agricultural University, Zhengzhou 450002, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, ChinaPeanut southern blight has a severe impact on peanut production and is one of the most devastating soil-borne fungal diseases. We conducted a hyperspectral analysis of the spectral responses of plants to peanut southern blight to provide theoretical support for detecting the severity of the disease via remote sensing. In this study, we collected leaf-level spectral data during the winter of 2021 and the spring of 2022 in a greenhouse laboratory. We explored the spectral response mechanisms of diseased peanut leaves and developed a method for assessing the severity of peanut southern blight disease by comparing the continuous wavelet transform (CWT) with traditional spectral indices and incorporating machine learning techniques. The results showed that the SVM model performed best and was able to effectively detect the severity of peanut southern blight when using CWT (WF<sub>770~780</sub>, 5) as an input feature. The overall accuracy (OA) of the modeling dataset was 91.8% and the kappa coefficient was 0.88. For the validation dataset, the OA was 90.5% and the kappa coefficient was 0.87. These findings highlight the potential of this CWT-based method for accurately assessing the severity of peanut southern blight.https://www.mdpi.com/2077-0472/13/8/1504peanut southern blightreflection spectrumspectral indexcontinuous wavelet transformmachine learning |
| spellingShingle | Wei Guo Heguang Sun Hongbo Qiao Hui Zhang Lin Zhou Ping Dong Xiaoyu Song Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine Learning Agriculture peanut southern blight reflection spectrum spectral index continuous wavelet transform machine learning |
| title | Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine Learning |
| title_full | Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine Learning |
| title_fullStr | Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine Learning |
| title_full_unstemmed | Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine Learning |
| title_short | Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine Learning |
| title_sort | spectral detection of peanut southern blight severity based on continuous wavelet transform and machine learning |
| topic | peanut southern blight reflection spectrum spectral index continuous wavelet transform machine learning |
| url | https://www.mdpi.com/2077-0472/13/8/1504 |
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