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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