Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet - Li, He; Shi, Hongtao; Du, Anghong; Mao, Yilin; Fan, Kai; Wang, Yu; Shen, Yaozong; Wang, Shuangshuang; Xu, Xiuxiu; Tian, Lili; Wang, Hui; Ding, Zhaotang (Frontiers in Plant Science, 2022)
Brown blight, target spot and tea coal disease are three major leaf diseases of tea plants, and apolygus lucorum is a major pest in tea garden. The traditional symptom recognition of tea leaf diseases and insect pests is mainly through manual identification, which has some problems, such as low accuracy, low efficiency, strong subjectivity and so on. Therefore, it is very necessary to find a method that can effectively identify tea plant diseases and pests. In this paper, we proposed a recognition framework of tea leaf disease and insect pest symptoms based on Mask R-CNN, wavelet transform and F-RNet. Firstly, Mask R-CNN model was used to segment disease spots and insect spots from tea leaves. Then, the two-dimensional discrete wavelet transform was used to enhance the features of the disease spot and insect spot image, so as to obtain the images with four frequencies. Finally, the images of four frequencies were simultaneously input into the four channels residual network (F-RNet) to identify symptoms of tea leaf diseases and insect pests. The results show that Mask R-CNN model can detect 98.7% of DSIS, which can ensure that almost all disease spots and insect spots can be extracted from leaves. The accuracy of F-RNet model is 88%, which is higher than that of the other models (SVM, AlexNet, VGG16 and ResNet18). Therefore, this experimental framework can accurately segment and identify the disease and insect spots of tea leaves, which is not only of great significance for the accurate identification of tea plant diseases and insect pests, but also of great value for further using artificial intelligence to carry out the comprehensive control of tea plant diseases and insect pests.