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Detecting Botrytis Cinerea Control Efficacy via Deep Learning

Author

Listed:
  • Wenlong Yi

    (School of Software, Jiangxi Agricultural University, Nanchang 330045, China)

  • Xunsheng Zhang

    (School of Software, Jiangxi Agricultural University, Nanchang 330045, China)

  • Shiming Dai

    (School of Software, Jiangxi Agricultural University, Nanchang 330045, China)

  • Sergey Kuzmin

    (Faculty of Computer Science and Technology, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg 197022, Russia)

  • Igor Gerasimov

    (Faculty of Computer Science and Technology, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg 197022, Russia)

  • Xiangping Cheng

    (Institute of Applied Physics, Jiangxi Academy of Sciences, Nanchang 330096, China)

Abstract

This study proposes a deep learning-based method for monitoring the growth of Botrytis cinerea and evaluating the effectiveness of control measures. It aims to address the limitations of traditional statistical analysis methods in capturing non-linear relationships and multi-factor synergistic effects. The method integrates colony growth environment data and images as network inputs, achieving real-time prediction of colony area through an improved RepVGG network. The innovations include (1) combining channel attention mechanism, multi-head self-attention mechanism, and multi-scale feature extractor to improve prediction accuracy and (2) introducing the Shapley value algorithm to achieve a precise quantitative analysis of environmental variables’ contribution to colony growth. Experimental results show that the validation loss of this method reaches 0.007, with a mean absolute error of 0.0148, outperforming other comparative models. This study enriches the theory of gray mold control and provides information technology for optimizing and selecting its inhibitors.

Suggested Citation

  • Wenlong Yi & Xunsheng Zhang & Shiming Dai & Sergey Kuzmin & Igor Gerasimov & Xiangping Cheng, 2024. "Detecting Botrytis Cinerea Control Efficacy via Deep Learning," Agriculture, MDPI, vol. 14(11), pages 1-16, November.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:11:p:2054-:d:1520987
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    References listed on IDEAS

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    1. Winter, Eyal, 2002. "The shapley value," Handbook of Game Theory with Economic Applications, in: R.J. Aumann & S. Hart (ed.), Handbook of Game Theory with Economic Applications, edition 1, volume 3, chapter 53, pages 2025-2054, Elsevier.
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