Tea disease identification based on ECA attention mechanism ResNet50 network
Addressing the challenge of identifying tea plant diseases against the complex background of tea gardens, this study proposes the ECA-ResNet50 model. By optimizing the ResNet50 architecture, adopting a multi-layer small convolution kernel strategy to enhance feature extraction capabilities, and intr...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1489655/full |
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| _version_ | 1832087182063435776 |
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| author | Lanting Li Yingding Zhao |
| author_facet | Lanting Li Yingding Zhao |
| author_sort | Lanting Li |
| collection | DOAJ |
| description | Addressing the challenge of identifying tea plant diseases against the complex background of tea gardens, this study proposes the ECA-ResNet50 model. By optimizing the ResNet50 architecture, adopting a multi-layer small convolution kernel strategy to enhance feature extraction capabilities, and introducing the ECA attention mechanism to focus on key features, the model achieves a 93.06% accuracy rate in tea disease identification, representing a 3.18% improvement over the original model, demonstrating industry-leading performance advantages. This model not only accurately identifies tea diseases in gardens but also possesses excellent generalization capabilities, performing outstandingly on datasets of other plant categories. These results indicate that ECA-ResNet50 can effectively mitigate the interference of complex backgrounds and precisely recognize tea disease targets. |
| format | Article |
| id | doaj-art-d843dda221ba47a281c5b6eaf0ff3b6a |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-d843dda221ba47a281c5b6eaf0ff3b6a2025-02-06T07:10:03ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-02-011610.3389/fpls.2025.14896551489655Tea disease identification based on ECA attention mechanism ResNet50 networkLanting LiYingding ZhaoAddressing the challenge of identifying tea plant diseases against the complex background of tea gardens, this study proposes the ECA-ResNet50 model. By optimizing the ResNet50 architecture, adopting a multi-layer small convolution kernel strategy to enhance feature extraction capabilities, and introducing the ECA attention mechanism to focus on key features, the model achieves a 93.06% accuracy rate in tea disease identification, representing a 3.18% improvement over the original model, demonstrating industry-leading performance advantages. This model not only accurately identifies tea diseases in gardens but also possesses excellent generalization capabilities, performing outstandingly on datasets of other plant categories. These results indicate that ECA-ResNet50 can effectively mitigate the interference of complex backgrounds and precisely recognize tea disease targets.https://www.frontiersin.org/articles/10.3389/fpls.2025.1489655/fulltea plant diseasesECA attention mechanismResNet50deep learningleave |
| spellingShingle | Lanting Li Yingding Zhao Tea disease identification based on ECA attention mechanism ResNet50 network Frontiers in Plant Science tea plant diseases ECA attention mechanism ResNet50 deep learning leave |
| title | Tea disease identification based on ECA attention mechanism ResNet50 network |
| title_full | Tea disease identification based on ECA attention mechanism ResNet50 network |
| title_fullStr | Tea disease identification based on ECA attention mechanism ResNet50 network |
| title_full_unstemmed | Tea disease identification based on ECA attention mechanism ResNet50 network |
| title_short | Tea disease identification based on ECA attention mechanism ResNet50 network |
| title_sort | tea disease identification based on eca attention mechanism resnet50 network |
| topic | tea plant diseases ECA attention mechanism ResNet50 deep learning leave |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1489655/full |
| work_keys_str_mv | AT lantingli teadiseaseidentificationbasedonecaattentionmechanismresnet50network AT yingdingzhao teadiseaseidentificationbasedonecaattentionmechanismresnet50network |