A deep learning model based on RGB and hyperspectral images for efficiently detecting tea green leafhopper damage symptoms
The tea green leafhopper, also known as ''Empoasca onukii Matsuda,'' is a common pest of tea plants that can cause significant economic losses when its damage becomes severe. However, traditional methods of recognizing and classifying the damage symptoms of this pest rely on huma...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525000516 |
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| author | Yang Xu Yilin Mao He Li Jiazhi Shen Xiuxiu Xu Shuangshuang Wang Shah Zaman Zhaotang Ding Yu Wang |
| author_facet | Yang Xu Yilin Mao He Li Jiazhi Shen Xiuxiu Xu Shuangshuang Wang Shah Zaman Zhaotang Ding Yu Wang |
| author_sort | Yang Xu |
| collection | DOAJ |
| description | The tea green leafhopper, also known as ''Empoasca onukii Matsuda,'' is a common pest of tea plants that can cause significant economic losses when its damage becomes severe. However, traditional methods of recognizing and classifying the damage symptoms of this pest rely on human visual observation, which is time-consuming, labor-intensive, and subjective. In this study, deep learning models were developed that use both RGB and hyperspectral images to rapidly recognize and classify the damage symptoms caused by the tea green leafhopper. The results showed that both RGB and hyperspectral images can effectively capture the different levels of damage to tea buds caused by the leafhopper. For RGB image classification, models were built using ResNet18, VGG16, AlexNet, WT-ResNet18, WT-VGG16, and WT-AlexNet. Among all these models, WT-VGG16 performed the best, achieving an accuracy of 80.0 %. For hyperspectral image classification, models were built using UVE-SVM, CARS-SVM, SPA-SVM, NONE-SVM, UVE-LSTM, CARS-LSTM, SPA-LSTM, and NONE-LSTM. Among all these models, SPA-LSTM performed the best, achieving an accuracy of 95.6 %. This study demonstrates the potential of using RGB and hyperspectral imaging to accurately and non-destructively monitor the occurrence of tea green leafhopper damage. Such monitoring methods could prove to be efficient in managing this pest. |
| format | Article |
| id | doaj-art-79d3831a01db4505b3cf20cfa6d32904 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-79d3831a01db4505b3cf20cfa6d329042025-02-07T04:48:31ZengElsevierSmart Agricultural Technology2772-37552025-03-0110100817A deep learning model based on RGB and hyperspectral images for efficiently detecting tea green leafhopper damage symptomsYang Xu0Yilin Mao1He Li2Jiazhi Shen3Xiuxiu Xu4Shuangshuang Wang5Shah Zaman6Zhaotang Ding7Yu Wang8College of Horticulture, Qingdao Agricultural University, Qingdao 266109, ChinaCollege of Horticulture, Qingdao Agricultural University, Qingdao 266109, ChinaCollege of Horticulture, Qingdao Agricultural University, Qingdao 266109, ChinaTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, ChinaTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, ChinaTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, ChinaSchool of Tea and Coffee & School of Bioinformatics and Engineering, Pu'er University, 6 Xueyuan Road, Pu'er 665000, ChinaTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; Corresponding authors.College of Horticulture, Qingdao Agricultural University, Qingdao 266109, China; Corresponding authors.The tea green leafhopper, also known as ''Empoasca onukii Matsuda,'' is a common pest of tea plants that can cause significant economic losses when its damage becomes severe. However, traditional methods of recognizing and classifying the damage symptoms of this pest rely on human visual observation, which is time-consuming, labor-intensive, and subjective. In this study, deep learning models were developed that use both RGB and hyperspectral images to rapidly recognize and classify the damage symptoms caused by the tea green leafhopper. The results showed that both RGB and hyperspectral images can effectively capture the different levels of damage to tea buds caused by the leafhopper. For RGB image classification, models were built using ResNet18, VGG16, AlexNet, WT-ResNet18, WT-VGG16, and WT-AlexNet. Among all these models, WT-VGG16 performed the best, achieving an accuracy of 80.0 %. For hyperspectral image classification, models were built using UVE-SVM, CARS-SVM, SPA-SVM, NONE-SVM, UVE-LSTM, CARS-LSTM, SPA-LSTM, and NONE-LSTM. Among all these models, SPA-LSTM performed the best, achieving an accuracy of 95.6 %. This study demonstrates the potential of using RGB and hyperspectral imaging to accurately and non-destructively monitor the occurrence of tea green leafhopper damage. Such monitoring methods could prove to be efficient in managing this pest.http://www.sciencedirect.com/science/article/pii/S2772375525000516Tea green leafhopperRGBHyperspectralMachine learningDeep learningClassification |
| spellingShingle | Yang Xu Yilin Mao He Li Jiazhi Shen Xiuxiu Xu Shuangshuang Wang Shah Zaman Zhaotang Ding Yu Wang A deep learning model based on RGB and hyperspectral images for efficiently detecting tea green leafhopper damage symptoms Smart Agricultural Technology Tea green leafhopper RGB Hyperspectral Machine learning Deep learning Classification |
| title | A deep learning model based on RGB and hyperspectral images for efficiently detecting tea green leafhopper damage symptoms |
| title_full | A deep learning model based on RGB and hyperspectral images for efficiently detecting tea green leafhopper damage symptoms |
| title_fullStr | A deep learning model based on RGB and hyperspectral images for efficiently detecting tea green leafhopper damage symptoms |
| title_full_unstemmed | A deep learning model based on RGB and hyperspectral images for efficiently detecting tea green leafhopper damage symptoms |
| title_short | A deep learning model based on RGB and hyperspectral images for efficiently detecting tea green leafhopper damage symptoms |
| title_sort | deep learning model based on rgb and hyperspectral images for efficiently detecting tea green leafhopper damage symptoms |
| topic | Tea green leafhopper RGB Hyperspectral Machine learning Deep learning Classification |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525000516 |
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