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Enhanced Sea Horse Optimization Algorithm for Hyperparameter Optimization of Agricultural Image Recognition

Author

Listed:
  • Zhuoshi Li

    (College of Plant Protection, Jilin Agricultural University, Changchun 130118, China
    College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Shizheng Qu

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Yinghang Xu

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Xinwei Hao

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Nan Lin

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

Abstract

Deep learning technology has made significant progress in agricultural image recognition tasks, but the parameter adjustment of deep models usually requires a lot of manual intervention, which is time-consuming and inefficient. To solve this challenge, this paper proposes an adaptive parameter tuning strategy that combines sine–cosine algorithm with Tent chaotic mapping to enhance sea horse optimization, which improves the search ability and convergence stability of standard sea horse optimization algorithm (SHO). Through adaptive optimization, this paper determines the best parameter configuration in ResNet-50 neural network and optimizes the model performance. The improved ESHO algorithm shows superior optimization effects than other algorithms in various performance indicators. The improved model achieves 96.7% accuracy in the corn disease image recognition task, and 96.4% accuracy in the jade fungus image recognition task. These results show that ESHO can not only effectively improve the accuracy of agricultural image recognition, but also reduce the need for manual parameter adjustment.

Suggested Citation

  • Zhuoshi Li & Shizheng Qu & Yinghang Xu & Xinwei Hao & Nan Lin, 2024. "Enhanced Sea Horse Optimization Algorithm for Hyperparameter Optimization of Agricultural Image Recognition," Mathematics, MDPI, vol. 12(3), pages 1-24, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:368-:d:1325045
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    References listed on IDEAS

    as
    1. Xiangpeng Fan & Zhibin Guan, 2023. "VGNet: A Lightweight Intelligent Learning Method for Corn Diseases Recognition," Agriculture, MDPI, vol. 13(8), pages 1-19, August.
    2. Zvi Drezner & Taly Dawn Drezner, 2020. "Biologically Inspired Parent Selection in Genetic Algorithms," Annals of Operations Research, Springer, vol. 287(1), pages 161-183, April.
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