Enhancing leaf disease classification using GAT-GCN hybrid model
Agriculture plays a critical role in the global economy, providing livelihoods and ensuring food security for billions. Progress in agricultural techniques has helped boost crop yield, along with a growing need for precise disease monitoring solutions. This requires accurate, efficient, and timely d...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1569821/full |
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| author | Shyam Sundhar Riya Sharma Priyansh Maheshwari Suvidha Rupesh Kumar T. Sunil Kumar |
| author_facet | Shyam Sundhar Riya Sharma Priyansh Maheshwari Suvidha Rupesh Kumar T. Sunil Kumar |
| author_sort | Shyam Sundhar |
| collection | DOAJ |
| description | Agriculture plays a critical role in the global economy, providing livelihoods and ensuring food security for billions. Progress in agricultural techniques has helped boost crop yield, along with a growing need for precise disease monitoring solutions. This requires accurate, efficient, and timely disease detection methods. The research presented in this paper addresses this need by analyzing a hybrid model built using Graph Attention Network (GAT) and Graph Convolution Network (GCN) models. The integration of these models has witnessed a notable improvement in the accuracy of leaf disease classification. GCN has been widely used for learning from graph-structured data, and GAT enhances this by incorporating attention mechanisms to focus on the most important neighbors. The methodology incorporates superpixel segmentation for efficient feature extraction, partitioning images into meaningful, homogeneous regions that better capture localized features. The robustness of the model is further enhanced by the edge augmentation technique. The edge augmentation technique in the context of graph has introduced a significant degree of generalization in the detection capabilities of the model as analyzed on apple, potato, and sugarcane leaves. To further optimize training, weight initialization techniques are applied. The hybrid model is evaluated against the individual performance of the GCN and GAT models and the hybrid model achieved a precision of 0.9822, recall of 0.9818, and F1-score of 0.9818 in apple leaf disease classification, a precision of 0.9746, recall of 0.9744, and F1-score of 0.9743 in potato leaf disease classification, and a precision of 0.8801, recall of 0.8801, and F1-score of 0.8799 in sugarcane leaf disease classification. The results indicate that the model is effective and consistent in identifying leaf diseases in plants. |
| format | Article |
| id | doaj-art-35c0b67658324e9482d0cdd13a3249fc |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-35c0b67658324e9482d0cdd13a3249fc2025-08-20T03:40:43ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-08-011610.3389/fpls.2025.15698211569821Enhancing leaf disease classification using GAT-GCN hybrid modelShyam Sundhar0Riya Sharma1Priyansh Maheshwari2Suvidha Rupesh Kumar3T. Sunil Kumar4School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, Tamil Nadu, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, Tamil Nadu, IndiaDepartment of Electrical Engineering, Mathematics and Science, University of Gävle, Gävle, SwedenAgriculture plays a critical role in the global economy, providing livelihoods and ensuring food security for billions. Progress in agricultural techniques has helped boost crop yield, along with a growing need for precise disease monitoring solutions. This requires accurate, efficient, and timely disease detection methods. The research presented in this paper addresses this need by analyzing a hybrid model built using Graph Attention Network (GAT) and Graph Convolution Network (GCN) models. The integration of these models has witnessed a notable improvement in the accuracy of leaf disease classification. GCN has been widely used for learning from graph-structured data, and GAT enhances this by incorporating attention mechanisms to focus on the most important neighbors. The methodology incorporates superpixel segmentation for efficient feature extraction, partitioning images into meaningful, homogeneous regions that better capture localized features. The robustness of the model is further enhanced by the edge augmentation technique. The edge augmentation technique in the context of graph has introduced a significant degree of generalization in the detection capabilities of the model as analyzed on apple, potato, and sugarcane leaves. To further optimize training, weight initialization techniques are applied. The hybrid model is evaluated against the individual performance of the GCN and GAT models and the hybrid model achieved a precision of 0.9822, recall of 0.9818, and F1-score of 0.9818 in apple leaf disease classification, a precision of 0.9746, recall of 0.9744, and F1-score of 0.9743 in potato leaf disease classification, and a precision of 0.8801, recall of 0.8801, and F1-score of 0.8799 in sugarcane leaf disease classification. The results indicate that the model is effective and consistent in identifying leaf diseases in plants.https://www.frontiersin.org/articles/10.3389/fpls.2025.1569821/fullleaf disease detectionGraph Convolution NetworksGraph Attention Networkshybrid modelapple leafsugarcane leaf |
| spellingShingle | Shyam Sundhar Riya Sharma Priyansh Maheshwari Suvidha Rupesh Kumar T. Sunil Kumar Enhancing leaf disease classification using GAT-GCN hybrid model Frontiers in Plant Science leaf disease detection Graph Convolution Networks Graph Attention Networks hybrid model apple leaf sugarcane leaf |
| title | Enhancing leaf disease classification using GAT-GCN hybrid model |
| title_full | Enhancing leaf disease classification using GAT-GCN hybrid model |
| title_fullStr | Enhancing leaf disease classification using GAT-GCN hybrid model |
| title_full_unstemmed | Enhancing leaf disease classification using GAT-GCN hybrid model |
| title_short | Enhancing leaf disease classification using GAT-GCN hybrid model |
| title_sort | enhancing leaf disease classification using gat gcn hybrid model |
| topic | leaf disease detection Graph Convolution Networks Graph Attention Networks hybrid model apple leaf sugarcane leaf |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1569821/full |
| work_keys_str_mv | AT shyamsundhar enhancingleafdiseaseclassificationusinggatgcnhybridmodel AT riyasharma enhancingleafdiseaseclassificationusinggatgcnhybridmodel AT priyanshmaheshwari enhancingleafdiseaseclassificationusinggatgcnhybridmodel AT suvidharupeshkumar enhancingleafdiseaseclassificationusinggatgcnhybridmodel AT tsunilkumar enhancingleafdiseaseclassificationusinggatgcnhybridmodel |