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Research on deep learning in apple leaf disease recognition
- Zhong, Yong, Zhao, Ming
- Computers and electronics in agriculture 2020 v.168 pp. 105146
- Malus domestica, apples, crop production, data collection, financial economics, foliar diseases, fruit growing, leaves, models
- The main reason affecting apple production is the occurrence of apple leaf diseases, which causes huge economic losses every year. Therefore, it is of great significance to study the identification of apple leaf diseases. Based on DenseNet-121 deep convolution network, three methods of regression, multi-label classification and focus loss function were proposed to identify apple leaf diseases. In this paper, the apple leaf image data set, including 2462 images of six apple leaf diseases, were used for data modeling and method evaluation. The proposed methods achieved 93.51%, 93.31% and 93.71% accuracy on the test set respectively, which were better than the traditional multi-classification method based on cross-entropy loss function with an accuracy of 92.29%.