DBA-DeepLab: Dual-Backbone Attention-Enhanced DeepLab V3+ Model for Plant Disease Segmentation.

Abstract

Accurate and efficient plant disease segmentation is crucial for early diagnosis and precision agriculture. In this study, we propose a DBA-DeepLab model, i.e., a Dual-Backbone Attention-Enhanced DeepLab model, which integrates DeepLabV3+ with dual backbones of ResNet-50 and EfficientNet-B3 and a Convolutional Block Attention Module (CBAM) for improved plant disease segmentation. The integration of multi-scale feature extraction, attention mechanisms, and edge preservation with the Sobel filter enhances the ability of the model to focus on disease-affected regions with more accuracy and reduce false positives and false negatives. The model was trained and validated using the PlantDoc dataset with a batch size of 32, Adam optimizer, and 50 epochs for better convergence and generalization. Experimental results show that the proposed DBA-DeepLab outperforms DeepLabV3+ with EfficientNet-B3 encoder, DeepLabV3+ with ResNet-50 encoder, and DeepLabV3+ with dual encoder (EfficientNet-B3 and ResNet-50) in terms of segmentation parameters. The proposed model yields 99.35% accuracy, a 91.48% Dice coefficient, an 85.85% IoU coefficient, 96.78% precision, and 100% recall, outperforming the state-of-the-art. Grad-CAM visualization was applied to validate the model's interpretability, affirming its capacity to highlight disease-affected regions and avoid background noise. Comparative analyses with these DeepLabV3+ variants support the improved generalization, segmentation accuracy, and robustness of the proposed model. These results show that DBA-DeepLab is an extremely efficient and scalable solution for plant disease segmentation, with potential applications in smart farming, automatic disease detection, and precision agriculture.

Plant phenotyping relevance

植物の病害領域・重症度を画像から分割推定する深層学習手法を提案し、データセット上で検証・既存モデル比較を行っており、病害表現型の取得手法が中心である。

Code and data availability

The paper's segmentation experiments were trained/validated on a public Kaggle leaf disease segmentation dataset (images with ground-truth masks), explicitly declared openly available in the Data Availability Statement. No author analysis code or trained model checkpoints are disclosed.