LCAT: A Lightweight Color-Aware Transformer With Hierarchical Attention for Leaf Disease Classification in Precision Agriculture
Leaf disease classification plays a critical role in ensuring healthy crop production and preventing agricultural losses. This research proposes a novel deep learning-based method for classifying leaf diseases using a dataset of 21,733 images across six distinct disease categories. We introduce the...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11086594/ |
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| Summary: | Leaf disease classification plays a critical role in ensuring healthy crop production and preventing agricultural losses. This research proposes a novel deep learning-based method for classifying leaf diseases using a dataset of 21,733 images across six distinct disease categories. We introduce the Lightweight Color-Aware Transformer (LCAT), a model designed to achieve high accuracy and computational efficiency for practical deployment, particularly in environments with limited hardware resources. LCAT uses a special attention system that combines color-focused attention with attention to different sizes of distortions, helping the model to accurately identify disease-related changes that usually appear as slight color changes and small deformations on leaves. A key contribution of LCAT lies in its use of smaller patch sizes to extract features in high-resolution regions while maintaining shallow depth, which significantly reduces model complexity. By focusing attention on a small, highly relevant region of leaf images, LCAT maintains strong classification performance without the computational overhead typically encountered in deeper transformer architectures. The experimental results show that LCAT outperforms standard Vision Transformer (ViT) models with a similar number of parameters. In particular, LCAT achieves a mean average precision (mAP) of 0.81 and a classification accuracy of 0.75, compared to ViT’s mAP of 0.75 and an accuracy of 0.68, while using significantly fewer floating-point operations (FLOPs), at 3.33G vs. 17.58G. This work highlights the potential of transformer-based models in plant pathology and provides a lightweight, high-performance alternative suitable for real-time field or mobile applications. Future work includes extending LCAT to support more crop species and plant diseases and integrating it into comprehensive decision support systems for precision agriculture. |
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| ISSN: | 2169-3536 |