期刊
SENSORS
卷 20, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/s20123535
关键词
apple leaf diseases; transfer learning; deep learning; convolutional neural networks
资金
- National Natural Science Foundation of China [61472282, 61672035, 61872004]
- Educational Commission of Anhui Province [KJ2019ZD05]
- Open Fund from Key Laboratory of Metallurgical Emission Reduction & Resources Recycling [KF2017-02]
- Co-Innovation Center for Information Supply and Assurance Technology in AHU [ADXXBZ201705]
- Anhui Scientific Research Foundation for Returnees
Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the rapid diagnosis and accurate identification of them play an important role in the development of apple production. In this work, an improved model based on VGG16 is proposed to identify apple leaf diseases, in which the global average poling layer is used to replace the fully connected layer to reduce the parameters and a batch normalization layer is added to improve the convergence speed. A transfer learning strategy is used to avoid a long training time. The experimental results show that the overall accuracy of apple leaf classification based on the proposed model can reach 99.01%. Compared with the classical VGG16, the model parameters are reduced by 89%, the recognition accuracy is improved by 6.3%, and the training time is reduced to 0.56% of that of the original model. Therefore, the deep convolutional neural network model proposed in this work provides a better solution for the identification of apple leaf diseases with higher accuracy and a faster convergence speed.
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