Identification of Coffee Leaf Pests and Diseases based on Transfer Learning and Knowledge Distillation

Zi Wang; Zheng Ren; Xue Li

doi:10.54097/fcis.v5i1.11537

Authors

Zi Wang
Zheng Ren
Xue Li

DOI:

https://doi.org/10.54097/fcis.v5i1.11537

Keywords:

Coffee Leaf, Transfer Learning, Knowledge Distillation

Abstract

The yield of coffee has a significant effect on the development of the economy. It is important to monitor the health status of coffee plants. Leaves can represent the growth of crops. Analysis leaf image is an effective method to monitor crop growth status. With the advancement of artificial intelligence technology, neural networks with strong learning ability have been proposed. They have high accuracy in identifying leaf pests and diseases. However, the structure of these networks is complex and the speed of computing is slow. They are not conducive to real-time analysis. For simple networks, it is difficult to achieve high recognition accuracy directly. To solve this problem, a lightweight model is designed for leaf image analysis. Leaf images are learned by VGG network with pre-trained weights on ImageNet. Use the VGG network as a teacher network. Then design a lightweight student network. Train student network with knowledge distillation method. A lightweight model with high recognition accuracy can be obtained. This research explored the effect of the method on the coffee leaf data set. Experiment proved that the accuracy of the proposed method is 96.73%. The accuracy was 4.29% higher than directly training. Meantime, the calculation speed of the model is quick. The proposed method is of great practical significance for identifying coffee leaf pests and diseases.

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