CNN transfer learning for automatic image-based classification of crop disease

As the latest breakthrough in the field of computer vision, deep convolutional neural network(CNN) is very promising for the classification of crop diseases. However, the common limitation applying the algorithm is reliance on a large amount of training data. In some cases, obtaining and labeling a large dataset might be difficult. We solve this problem both from the network size and the training mechanism. In this paper, using 2430 images from the natural environment, which contain 2 crop species and 8 diseases, 6 kinds of CNN with different depths are trained to investigate appropriate structure. In order to address the over-fitting problem caused by our small-scale dataset, we systemically analyze the performances of training from scratch and using transfer learning. In case of transfer learning, we first train PlantVillage dataset to get a pre-trained model, and then retrain our dataset based on this model to adjust parameters. The CNN with 5 convolutional layers achieves an accuracy of 90.84% by using transfer learning. Experimental results demonstrate that the combination of CNN and transfer learning is effective for crop disease images classification with small-scale dataset.