PlantAIM: A new baseline model integrating global attention and local features for enhanced plant disease identification
Plant diseases significantly affect the quality and yield of agricultural production. Conventionally, detection has relied on plant pathologists, but recent advances in deep learning, particularly the Vision Transformer (ViT) and Convolutional Neural Network (CNN), have made it feasible for automate...
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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/S2772375525000474 |
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| author | Abel Yu Hao Chai Sue Han Lee Fei Siang Tay Hervé Goëau Pierre Bonnet Alexis Joly |
| author_facet | Abel Yu Hao Chai Sue Han Lee Fei Siang Tay Hervé Goëau Pierre Bonnet Alexis Joly |
| author_sort | Abel Yu Hao Chai |
| collection | DOAJ |
| description | Plant diseases significantly affect the quality and yield of agricultural production. Conventionally, detection has relied on plant pathologists, but recent advances in deep learning, particularly the Vision Transformer (ViT) and Convolutional Neural Network (CNN), have made it feasible for automated plant disease identification. Despite their prominence, there are still significant gaps in our understanding of how these models differ in feature extraction and representation, particularly in complex multi-crop disease identification tasks. This challenge arises from the simultaneous need to learn crop-specific and disease-specific features for accurate identification of crop species and its associated diseases. To address this, we introduce Plant Disease Global-Local Features Fusion Attention Model (PlantAIM), a new hybrid framework that fuses global attention mechanisms of ViT with local feature extraction capabilities of CNN. PlantAIM aims to improve the model's ability to simultaneously learn and focus on crop-specific and disease-specific features. We conduct extensive evaluations to assess the robustness and generalizability of PlantAIM compared to state-of-the-art (SOTA) models, including scenarios with limited training samples and real-world environmental data. Our results show that PlantAIM achieves superior performance. This research not only deepens our understanding of feature learning for ViT and CNN models, but also sets a new benchmark in the dynamic field of plant disease identification. The code is available at github: PlantAIM |
| format | Article |
| id | doaj-art-b552711e0c7c4f5da5f2c69b65415114 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-b552711e0c7c4f5da5f2c69b654151142025-02-07T04:48:31ZengElsevierSmart Agricultural Technology2772-37552025-03-0110100813PlantAIM: A new baseline model integrating global attention and local features for enhanced plant disease identificationAbel Yu Hao Chai0Sue Han Lee1Fei Siang Tay2Hervé Goëau3Pierre Bonnet4Alexis Joly5Swinburne University of Technology Sarawak Campus, Q5B, Kuching, 93250, Sarawak, Malaysia; Corresponding author.Swinburne University of Technology Sarawak Campus, Q5B, Kuching, 93250, Sarawak, MalaysiaSwinburne University of Technology Sarawak Campus, Q5B, Kuching, 93250, Sarawak, MalaysiaAMAP, Univ Montpellier, IRD, CNRS, INRAE, CIRAD, Montpellier, FranceAMAP, Univ Montpellier, IRD, CNRS, INRAE, CIRAD, Montpellier, FranceINRIA, Montpellier, FrancePlant diseases significantly affect the quality and yield of agricultural production. Conventionally, detection has relied on plant pathologists, but recent advances in deep learning, particularly the Vision Transformer (ViT) and Convolutional Neural Network (CNN), have made it feasible for automated plant disease identification. Despite their prominence, there are still significant gaps in our understanding of how these models differ in feature extraction and representation, particularly in complex multi-crop disease identification tasks. This challenge arises from the simultaneous need to learn crop-specific and disease-specific features for accurate identification of crop species and its associated diseases. To address this, we introduce Plant Disease Global-Local Features Fusion Attention Model (PlantAIM), a new hybrid framework that fuses global attention mechanisms of ViT with local feature extraction capabilities of CNN. PlantAIM aims to improve the model's ability to simultaneously learn and focus on crop-specific and disease-specific features. We conduct extensive evaluations to assess the robustness and generalizability of PlantAIM compared to state-of-the-art (SOTA) models, including scenarios with limited training samples and real-world environmental data. Our results show that PlantAIM achieves superior performance. This research not only deepens our understanding of feature learning for ViT and CNN models, but also sets a new benchmark in the dynamic field of plant disease identification. The code is available at github: PlantAIMhttp://www.sciencedirect.com/science/article/pii/S2772375525000474Plant disease identificationVision transformerConvolutional neural networkGrad-CAM visualization analysisHybrid model |
| spellingShingle | Abel Yu Hao Chai Sue Han Lee Fei Siang Tay Hervé Goëau Pierre Bonnet Alexis Joly PlantAIM: A new baseline model integrating global attention and local features for enhanced plant disease identification Smart Agricultural Technology Plant disease identification Vision transformer Convolutional neural network Grad-CAM visualization analysis Hybrid model |
| title | PlantAIM: A new baseline model integrating global attention and local features for enhanced plant disease identification |
| title_full | PlantAIM: A new baseline model integrating global attention and local features for enhanced plant disease identification |
| title_fullStr | PlantAIM: A new baseline model integrating global attention and local features for enhanced plant disease identification |
| title_full_unstemmed | PlantAIM: A new baseline model integrating global attention and local features for enhanced plant disease identification |
| title_short | PlantAIM: A new baseline model integrating global attention and local features for enhanced plant disease identification |
| title_sort | plantaim a new baseline model integrating global attention and local features for enhanced plant disease identification |
| topic | Plant disease identification Vision transformer Convolutional neural network Grad-CAM visualization analysis Hybrid model |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525000474 |
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