Scholarlink

<p>In recent years, the production of China’s four major crops has declined due to pests and diseases. This situation poses a serious challenge to food security. Therefore, timely detection and prevention of diseases is essential. First, we apply data enhancement techniques to augment the data to improve the generalization ability of the model. Secondly, to reduce the model parameters and make it easier to deploy on end devices, we use the knowledge distillation method. Finally, a method of dynamically adjusting the parameter T according to the loss value (DYTKD) is proposed to improve the performance of the model further. The experiment shows that knowledge distillation can reduce the number of parameters while ensuring that the accuracy of the student model closely matches that of the teacher model, achieving 98.94% accuracy Meanwhile, data augmentation can also enhance the accuracy of the model by 6.83%. Compared with the basic knowledge distillation method, the accuracy of DYTKD was increased by 1.3% without altering the student network and other parameters, and the accuracy of pest identification and classification was effectively improved. Among 1342 pest pictures, 1221 were correctly identified and accurately classified. Our codes are available at <a href="https://github.com/wln130221/DYTKD">https://github.com/wln130221/DYTKD</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Application of knowledge distillation method with dynamic adjustment of temperature parameters in pest classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月30日

查看全文
  • Linan Wang,
  • Hongmin Zhao

摘要

In recent years, the production of China’s four major crops has declined due to pests and diseases. This situation poses a serious challenge to food security. Therefore, timely detection and prevention of diseases is essential. First, we apply data enhancement techniques to augment the data to improve the generalization ability of the model. Secondly, to reduce the model parameters and make it easier to deploy on end devices, we use the knowledge distillation method. Finally, a method of dynamically adjusting the parameter T according to the loss value (DYTKD) is proposed to improve the performance of the model further. The experiment shows that knowledge distillation can reduce the number of parameters while ensuring that the accuracy of the student model closely matches that of the teacher model, achieving 98.94% accuracy Meanwhile, data augmentation can also enhance the accuracy of the model by 6.83%. Compared with the basic knowledge distillation method, the accuracy of DYTKD was increased by 1.3% without altering the student network and other parameters, and the accuracy of pest identification and classification was effectively improved. Among 1342 pest pictures, 1221 were correctly identified and accurately classified. Our codes are available at https://github.com/wln130221/DYTKD.