Rice Disease Recognition and Feature Visualization Using a Convolutional Neural Network

Abstract

To achieve fast and accurate identification of rice diseases in the field, we propose an automatic rice disease classifier, in which the process of characterizing rice diseases is visualized and analyzed by a deconvolutional neural network. An AlexNet model, pretrained by ImageNet, is constructed and trained on rice disease images to classify them. After the training is completed, the signal is repositioned to the corresponding position of the input image by a deconvolutional neural network corresponding to the AlexNet structure. The set of pixels that contribute most to the prediction of the convolutional neural network is identified from the deconvolution visualization map. The experimental results demonstrated the effectiveness of the proposed method. The classifier achieved an accuracy of 90.03% for the rice disease dataset, which was 8.39% and 16.78% higher than the accuracies achieved by the LeNet and BP neural networks, respectively. The features of the middle layer of the convolutional neural network perform a hierarchical transformation from low-level information, such as color, to high-level information, such as contours and edges of disease spots. This transformation process matches the criteria for the actual identification of rice diseases. The proposed method lays the foundation for the accurate identification of crop diseases and the design and adjustment of deep convolutional neural network structures.