Crop Disease Image Classification Based on Transfer Learning with DCNNs
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 overfitting. 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.
Transfer learning Deep learning Image classification DCNN Crop diseases
Yuan Yuan Sisi Fang Lei Chen
Institute of Intelligent Machines,Chinese Academy of Sciences,Hefei,China Institute of Intelligent Machines,Chinese Academy of Sciences,Hefei,China;University of Science and
国际会议
广州
英文
457-468
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）