Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural Network - Zhou, Huiru; Deng, Jie; Cai, Dingzhou; Lv, Xuan; Wu, Bo Ming (Frontiers in Plant Science, 2022)
In recent years, convolution neural network has been the most widely used deep learning algorithm in the field of plant disease diagnosis, and has performed well in classification. However, in practice, there are still some specific issues that have not been paid adequate attention to. For instance, the same pathogen may cause similar or different symptoms when infecting plant leaves, while the same pathogen may cause similar or disparate symptoms on different parts of plant. Therefore, questions come up naturally: should the images showing different symptoms of the same disease be in one class or two separate classes in the image database? And how will the different classification methods affect the results of image recognition? In this study, taking rice leaf blast and neck blast caused by Magnaporthe oryzae, and rice sheath blight caused by Rhizoctonia solani as examples, three experiments were designed to explore how database configuration affects recognition accuracy in recognizing different symptoms of the same disease on the same plant part, similar symptoms of the same disease on different parts and different symptoms on different parts. The results suggested that when the symptoms of same class were the same or similar, no matter whether they were on the same plant part or not, training combined classes of these images can get better performance than training them separately. When the difference between symptoms was obvious, the classification was relatively easy, separate training or combined training could both achieve relatively high recognition accuracy. The results also to a certain extent indicated that the greater the number of images in training data set, the higher was the average classification accuracy.