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Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet

文献类型： 外文期刊

作者： Li, He 1 ; Shi, Hongtao 2 ; Du, Anghong 2 ; Mao, Yilin 1 ; Fan, Kai 1 ; Wang, Yu 1 ; Shen, Yaozong 1 ; Wang, Shuangshuang 3 ; Xu, Xiuxiu 3 ; Tian, Lili 3 ; Wang, Hui 4 ; Ding, Zhaotang 1 ;

作者机构： 1.Qingdao Agr Univ, Tea Res Inst, Qingdao, Peoples R China

2.Qingdao Agr Univ, Sch Sci & Informat Sci, Qingdao, Peoples R China

3.Shandong Acad Agr Sci, Tea Res Inst, Jinan, Peoples R China

4.Rizhao Acad Agr Sci, Tea Res Inst, Rizhao, Peoples R China

关键词： tea plant; disease and pest stress; Mask R-CNN; wavelet transform; F-RNet

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： Brown blight, target spot, and tea coal diseases are three major leaf diseases of tea plants, and Apolygus lucorum is a major pest in tea plantations. 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 could effectively identify tea plants diseases and pests. In this study, we proposed a recognition framework of tea leaf disease and insect pest symptoms based on Mask R-CNN, wavelet transform and F-RNet. First, Mask R-CNN model was used to segment disease spots and insect spots from tea leaves. Second, the two-dimensional discrete wavelet transform was used to enhance the features of the disease spots and insect spots images, so as to obtain the images with four frequencies. Finally, the images of four frequencies were simultaneously input into the four-channeled residual network (F-RNet) to identify symptoms of tea leaf diseases and insect pests. The results showed that Mask R-CNN model could detect 98.7% of DSIS, which ensure that almost 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 (like SVM, AlexNet, VGG16 and ResNet18). Therefore, this experimental framework can accurately segment and identify diseases and insect spots of tea leaves, which 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.

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