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Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine Learning

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  • Wei Guo

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Heguang Sun

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
    Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China)

  • Hongbo Qiao

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Hui Zhang

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Lin Zhou

    (College of Plant Protection, Henan Agricultural University, Zhengzhou 450002, China)

  • Ping Dong

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Xiaoyu Song

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China)

Abstract

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 770~780 , 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.

Suggested Citation

  • Wei Guo & Heguang Sun & Hongbo Qiao & Hui Zhang & Lin Zhou & Ping Dong & Xiaoyu Song, 2023. "Spectral Detection of Peanut Southern Blight Severity Based on Continuous Wavelet Transform and Machine Learning," Agriculture, MDPI, vol. 13(8), pages 1-15, July.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1504-:d:1204288
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    Cited by:

    1. Heguang Sun & Lin Zhou & Meiyan Shu & Jie Zhang & Ziheng Feng & Haikuan Feng & Xiaoyu Song & Jibo Yue & Wei Guo, 2024. "Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation," Agriculture, MDPI, vol. 14(3), pages 1-18, March.

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