Identification of Maize Diseases Based on Dynamic Convolution and Tri-Attention Mechanism
Accurate, non-destructive classification of maize diseases is crucial for efficiently managing agricultural losses. While existing methods perform well in controlled environment dataset like PlantVillage, their accuracy often declines in real-world scenarios. In this work, ResNet50 is enhanced by in...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10824797/ |
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| author | Feilong Tang Rosalyn R. Porle Hoe Tung Yew Farrah Wong |
| author_facet | Feilong Tang Rosalyn R. Porle Hoe Tung Yew Farrah Wong |
| author_sort | Feilong Tang |
| collection | DOAJ |
| description | Accurate, non-destructive classification of maize diseases is crucial for efficiently managing agricultural losses. While existing methods perform well in controlled environment dataset like PlantVillage, their accuracy often declines in real-world scenarios. In this work, ResNet50 is enhanced by integrating a dynamic convolution module and triplet attention modules. This method adaptively recalibrates the convolution kernel weights, establishing dependencies across spatial and channel dimensions through tensor rotation and residual transformations. The proposed method surpasses state-of-the-art alternatives, reaching 98.79% validation accuracy on the PlantVillage maize dataset and 97.47% on the Corn Leaf Disease Dataset through cross-validation. Even with complex backgrounds, it attains an average accuracy of 88.33% for classifying six types of maize diseases. Experimental results confirm its effectiveness in maize disease detection. |
| format | Article |
| id | doaj-art-3f17da9a50bc4ca8b03e81da7f28fb5c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3f17da9a50bc4ca8b03e81da7f28fb5c2025-01-15T00:02:33ZengIEEEIEEE Access2169-35362025-01-01136834684410.1109/ACCESS.2025.352566110824797Identification of Maize Diseases Based on Dynamic Convolution and Tri-Attention MechanismFeilong Tang0https://orcid.org/0009-0009-0407-7789Rosalyn R. Porle1https://orcid.org/0000-0002-4600-3034Hoe Tung Yew2https://orcid.org/0000-0003-1579-3640Farrah Wong3Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, MalaysiaFaculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, MalaysiaFaculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, MalaysiaFaculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, MalaysiaAccurate, non-destructive classification of maize diseases is crucial for efficiently managing agricultural losses. While existing methods perform well in controlled environment dataset like PlantVillage, their accuracy often declines in real-world scenarios. In this work, ResNet50 is enhanced by integrating a dynamic convolution module and triplet attention modules. This method adaptively recalibrates the convolution kernel weights, establishing dependencies across spatial and channel dimensions through tensor rotation and residual transformations. The proposed method surpasses state-of-the-art alternatives, reaching 98.79% validation accuracy on the PlantVillage maize dataset and 97.47% on the Corn Leaf Disease Dataset through cross-validation. Even with complex backgrounds, it attains an average accuracy of 88.33% for classifying six types of maize diseases. Experimental results confirm its effectiveness in maize disease detection.https://ieeexplore.ieee.org/document/10824797/Attention mechanismdynamic convolutionfine-grained visual classificationmaize leaf diseaseresidual network |
| spellingShingle | Feilong Tang Rosalyn R. Porle Hoe Tung Yew Farrah Wong Identification of Maize Diseases Based on Dynamic Convolution and Tri-Attention Mechanism IEEE Access Attention mechanism dynamic convolution fine-grained visual classification maize leaf disease residual network |
| title | Identification of Maize Diseases Based on Dynamic Convolution and Tri-Attention Mechanism |
| title_full | Identification of Maize Diseases Based on Dynamic Convolution and Tri-Attention Mechanism |
| title_fullStr | Identification of Maize Diseases Based on Dynamic Convolution and Tri-Attention Mechanism |
| title_full_unstemmed | Identification of Maize Diseases Based on Dynamic Convolution and Tri-Attention Mechanism |
| title_short | Identification of Maize Diseases Based on Dynamic Convolution and Tri-Attention Mechanism |
| title_sort | identification of maize diseases based on dynamic convolution and tri attention mechanism |
| topic | Attention mechanism dynamic convolution fine-grained visual classification maize leaf disease residual network |
| url | https://ieeexplore.ieee.org/document/10824797/ |
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