Welcome to Smart Agriculture 中文

Smart Agriculture ›› 2019, Vol. 1 ›› Issue (3): 46-55.doi: 10.12133/j.smartag.2019.1.3.201903-SA005

• Information Perception and Acquisition • Previous Articles     Next Articles

Method for identifying crop disease based on CNN and transfer learning

Li Miao1, Wang Jingxian1,2, Li Hualong1, Hu Zelin1, Yang XuanJiang1,2, Huang Xiaoping1,2, Zeng Weihui1, Zhang Jian1,*(), Fang Sisi1,2   

  1. 1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China
    2. University of Science and Technology of China, Hefei 230026, China
  • Received:2019-03-29 Revised:2019-04-17 Online:2019-07-30 Published:2019-08-23
  • corresponding author: Jian Zhang E-mail:jzhang@iim.ac.cn


The internet is a huge resource base and a rich knowledge base. Aiming at the problem of small agricultural samples, the utilization technology of network resources was studied in the research, which would provide an idea and method for the research and application of crop disease identification and diagnosis. The knowledge transfer and deep learning methods to carry out research and experiments on public data sets (ImageNet, PlantVillage) and laboratory small sample disease data (AES-IMAGE) were introduced: first the batch normalization algorithm was applied to the AlexNet and VGG of Convolutional Neural Network (CNN) models to improve the over-fitting problem of the network; second the transfer learning strategy using parameter fine-tuning: The PlantVillage large-scale plant disease dataset was used to obtain the pre-trained model. On the improved network (AlexNet, VGG model), the pre-trained model was adjusted by our small sample dataset AES-IMAGE to obtain the disease identification model of cucumber and rice; third the transfer learning strategy was used for the bottleneck feature extraction: using the ImageNet big dataset to obtain the network parameters, CNN model (Inception-v3 and Mobilenet) was used as feature extractor to extract disease features. This method requires only a quick identification of the disease on the CPU and does not require a lot of training time, which can quickly complete the process of disease identification on the CPU. The experimental results show that: first in the transfer learning strategy of parameter fine-tuning: the highest accuracy rate was 98.33%, by using the VGG network parameter fine-tuning strategy; second in the transfer learning strategy of bottleneck feature extraction, using the Mobilenet model for bottleneck layer feature extraction and identification could obtain 96.8% validation accuracy. The results indicate that the combination of CNN and transfer learning is effective for the identification of small sample crop diseases.

Key words: CNN, crop diseases, overfitting, transfer learning, parameter fine-tuning, feature extractor

CLC Number: