外文摘要：In agriculture, early and timely detection and identification of plant disease categories can help growers take timely countermeasures. The use of deep learning techniques for plant disease category detection prevents further spread of the disease and helps to prevent crop production losses. In this paper, Based on the Next-Vit neural network model, we proposed a lightweight neural network CAST-Net based on the combination of convolution and self-attention, and we adopted self-distillation based on this model to achieve increased accuracy in classifying plant leaf diseases while reducing the number of model parameters and flops. Our model and method achieved 98.4% accuracy on the tomato subset of the data-enhanced PlantVillage dataset, a 4.9% improvement over the Next-Vit model, and 99.0% accuracy on the full PlantVillage set, a 6.9% improvement over the Next-Vit model. We also propose a new dynamic learning rate function that is applied to the training phase to prevent the loss from reaching the optimal value. The results show that our model and method have higher accuracy, fewer parameters, shorter training time and lower computational complexity than existing models.
外文关键词：plant disease classification;CAST-Net;Lightweight neural network;Dynamic learning rate function;Self -distillation
作者：Li, Yang;Zhao, Yun;Wu, Na;Xu, Xing
作者单位：Zhejiang Univ Sci & Technol
期刊名称：CROP PROTECTION
期刊影响因子：0.0
出版年份：2024
出版刊次：180
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台