Detecting Botrytis Cinerea Control Efficacy via Deep Learning

Wenlong Yi, Xunsheng Zhang, Shiming Dai, Sergey Kuzmin, Igor Gerasimov and Xiangping Cheng ()
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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

Agriculture, 2024, vol. 14, issue 11, 1-16

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.

Keywords: Botrytis cinerea; deep learning; attention mechanism; multi-scale features; Shapley (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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