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Crop Disease Image Classification Based on Transfer Learning with DCNNs

  • Yuan Yuan
  • Sisi Fang
  • Lei Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11257)

Abstract

Machine learning has been widely used in the crop disease image classification. Traditional methods relying on the extraction of hand-crafted low-level image features are difficulty to get satisfactory results. Deep convolutional neural network can deal with this problem because of automatically learning the feature representations from raw image data, but require enough labeled data to obtain a good generalization performance. However, in the field of agriculture, the available labeled data in target task is limited. In order to solve this problem, this paper proposes a method which combines transfer learning with two popular deep learning architectures (i.e., AlexNet and VGGNet) to classify eight kinds of crop diseases images. First, during the training procedure, the batch normalization and DisturbLabel techniques are introduced into these two networks to reduce the number of training iterations and over-fitting. Then, after training the pre-trained model by using the open source dataset PlantVillage. Finally, we fine-tune this model with our relatively small dataset preprocessed by a proposed strategy. The experimental results reveal that our approach can achieve an average accuracy of 95.93% compared to state-of-the-art method for our relatively small dataset, demonstrating the feasibility and robustness of this approach.

Keywords

Transfer learning Deep learning Image classification DCNN Crop diseases 

Notes

Acknowledgments

The authors would like to thank the anonymous reviewers for their helpful reviews. The work is supported by National Natural Science Foundation of China (Grant No. 31871521), the Open Project of Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, China (2017AIOT-01) and the 13th Five-year Informatization Plan of Chinese Academy of Sciences (Grant No. XXH13505-03-104).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Intelligent MachinesChinese Academy of SciencesHefeiChina
  2. 2.University of Science and Technology of ChinaHefeiChina

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