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- Title
基于知识蒸馏和模型剪枝的轻量化模型植物病害识别.
- Authors
刘媛媛; 王定坤; 邬雷; 黄德昌; 朱路
- Abstract
The emergence of deep learning has provided a new method for plant disease identification, but the current deep learning models have many parameters, which are difficult to use on edge devices such as smartphones or embedded sensor nodes with limited storage and computing resources. In the present study, plant leaves are taken as the research objects, and the methods based on knowledge distillation and model pruning are used to construct a light-weight model for plant disease identification. By improving the ResNet model, one or more teaching assistant network training models are introduced into the knowledge distillation. After sparse training, a light-weight student network model is obtained by model pruning; and the student network is retrained by using the teaching assistant network and learning rate rewinding, which can reduce the size of the model and effectively ensure the performance of the model. The experimental results show that, on a dataset including 38 categories of 14 plants, after pruning the model by 90%, the accuracy of the model is 97.78%, with an increase of 1.49 percentage points over the original model. On the dataset including 5 categories of apple leaves, after pruning the model by 70%, the accuracy of the model is 91.94%, which is 4.85 percentage points higher than the original model. The proposed light-weight model can be transplanted on Android platform and run effectively, which provides a new solution for the embedded terminal to accurately identify plant diseases.
- Publication
Acta Agriculturae Zhejiangensis, 2023, Vol 35, Issue 9, p2250
- ISSN
1004-1524
- Publication type
Article
- DOI
10.3969/j.issn.1004-1524.20221193