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Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation

Author

Listed:
  • Heguang Sun

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
    Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100094, China)

  • Lin Zhou

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

  • Meiyan Shu

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Jie Zhang

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

  • Ziheng Feng

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

  • Haikuan Feng

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

  • Xiaoyu Song

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

  • Jibo Yue

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Wei Guo

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

Abstract

Southern blight significantly impacts peanut yield, and its severity is exacerbated by high-temperature and high-humidity conditions. The mycelium attached to the plant’s interior quickly proliferates, contributing to the challenges of early detection and data acquisition. In recent years, the integration of machine learning and remote sensing data has become a common approach for disease monitoring. However, the poor quality and imbalance of data samples can significantly impact the performance of machine learning algorithms. This study employed the Synthetic Minority Oversampling Technique (SMOTE) algorithm to generate samples with varying severity levels. Additionally, it utilized Fractional-Order Differentiation (FOD) to enhance spectral information. The validation and testing of the 1D-CNN, SVM, and KNN models were conducted using experimental data from two different locations. In conclusion, our results indicate that the SMOTE-FOD-1D-CNN model enhances the ability to monitor the severity of peanut white mold disease (validation OA = 88.81%, Kappa = 0.85; testing OA = 82.76%, Kappa = 0.75).

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:3:p:476-:d:1357980
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    References listed on IDEAS

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    1. Joachims, Thorsten, 1998. "Making large-scale SVM learning practical," Technical Reports 1998,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. 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.
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