Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet
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 l...
Main Authors: | , , , , , , , , , , , |
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Format: | Article |
Language: | English |
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Frontiers Media S.A.
2022-07-01
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Series: | Frontiers in Plant Science |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.922797/full |
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author | He Li Hongtao Shi Anghong Du Yilin Mao Kai Fan Yu Wang Yaozong Shen Shuangshuang Wang Xiuxiu Xu Lili Tian Hui Wang Zhaotang Ding Zhaotang Ding |
author_facet | He Li Hongtao Shi Anghong Du Yilin Mao Kai Fan Yu Wang Yaozong Shen Shuangshuang Wang Xiuxiu Xu Lili Tian Hui Wang Zhaotang Ding Zhaotang Ding |
author_sort | He Li |
collection | DOAJ |
description | 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. |
first_indexed | 2024-04-13T20:16:40Z |
format | Article |
id | doaj.art-b6a299e9e46840dabf84e22a2943713a |
institution | Directory Open Access Journal |
issn | 1664-462X |
language | English |
last_indexed | 2024-04-13T20:16:40Z |
publishDate | 2022-07-01 |
publisher | Frontiers Media S.A. |
record_format | Article |
series | Frontiers in Plant Science |
spelling | doaj.art-b6a299e9e46840dabf84e22a2943713a2022-12-22T02:31:42ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-07-011310.3389/fpls.2022.922797922797Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNetHe Li0Hongtao Shi1Anghong Du2Yilin Mao3Kai Fan4Yu Wang5Yaozong Shen6Shuangshuang Wang7Xiuxiu Xu8Lili Tian9Hui Wang10Zhaotang Ding11Zhaotang Ding12Tea Research Institute, Qingdao Agricultural University, Qingdao, ChinaSchool of Science and Information Science, Qingdao Agricultural University, Qingdao, ChinaSchool of Science and Information Science, Qingdao Agricultural University, Qingdao, ChinaTea Research Institute, Qingdao Agricultural University, Qingdao, ChinaTea Research Institute, Qingdao Agricultural University, Qingdao, ChinaTea Research Institute, Qingdao Agricultural University, Qingdao, ChinaTea Research Institute, Qingdao Agricultural University, Qingdao, ChinaTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, ChinaTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, ChinaTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, ChinaTea Research Institute, Rizhao Academy of Agricultural Sciences, Rizhao, ChinaTea Research Institute, Qingdao Agricultural University, Qingdao, ChinaTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, ChinaBrown 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.https://www.frontiersin.org/articles/10.3389/fpls.2022.922797/fulltea plantdisease and pest stressMask R-CNNwavelet transformF-RNet |
spellingShingle | He Li Hongtao Shi Anghong Du Yilin Mao Kai Fan Yu Wang Yaozong Shen Shuangshuang Wang Xiuxiu Xu Lili Tian Hui Wang Zhaotang Ding Zhaotang Ding Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet Frontiers in Plant Science tea plant disease and pest stress Mask R-CNN wavelet transform F-RNet |
title | Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet |
title_full | Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet |
title_fullStr | Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet |
title_full_unstemmed | Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet |
title_short | Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet |
title_sort | symptom recognition of disease and insect damage based on mask r cnn wavelet transform and f rnet |
topic | tea plant disease and pest stress Mask R-CNN wavelet transform F-RNet |
url | https://www.frontiersin.org/articles/10.3389/fpls.2022.922797/full |
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