HAMNet: Hierarchical Multi-scale Attention Network for precise disease detection in pearl millet using spatial fusion.

Abstract

Pearl millet is a vital crop in arid and semi-arid regions, but its productivity is significantly impacted by fungal and bacterial diseases. Traditional disease detection methods, including manual inspection and conventional machine learning techniques, often suffer from inefficiencies due to subjectivity, time consumption, and limited feature extraction capabilities. Deep learning-based segmentation models such as U-Net, SegNet, DeepLabV3, and FCN have been employed to automate disease identification, but they exhibit limitations in accurately localizing disease-affected regions, handling complex spatial patterns, and maintaining high segmentation precision. To address these challenges, this study proposes HAMNet-Mask R-CNN, an AI-powered hierarchical multi-scale attention network integrated with a spectral-spatial fusion module for precise pearl millet disease detection. The novel framework enhances disease localization by utilizing hierarchical multi-scale feature learning, where low, mid, and high-level features contribute to superior classification accuracy. The attention mechanism prioritizes critical disease regions, reducing false positives, while Mask R-CNN enables pixel-wise segmentation, refining disease boundary identification. The proposed model is implemented using Python and TensorFlow with high-resolution image datasets. Performance evaluation demonstrates HAMNet-Mask R-CNN achieving a 99.35% Dice score, 98.80% IoU, 99.75% Precision, 99.78% Recall, and 99.65% Accuracy, outperforming U-Net (95.03% Dice, 90.53% IoU), SegNet (94.58% Dice, 89.73% IoU), DeepLabV3 (98.31% Dice, 96.69% IoU) and FCN(98.47% Dice score, 97.00% IoU). The results confirm the model's superiority in disease segmentation and classification, offering a robust and scalable solution for real-time disease monitoring in smart agriculture. The integration of hierarchical attention and spectral-spatial fusion significantly enhances detection accuracy, ensuring reliable disease identification and contributing to improved agricultural productivity.

Plant phenotyping relevance

真珠粟の病変領域を画像から画素単位で抽出・分類する深層学習手法を開発し、既存モデルと性能比較しているため、植物病害状態のフェノタイピング手法が中心である。

Code and data availability

The paper's authors explicitly state that the custom HAMNet–Mask R-CNN implementation code (preprocessing, training, evaluation, visualization) is publicly available on GitHub. The pearl millet leaf image dataset used as phenotyping input is also publicly hosted on Roboflow Universe and cited as the data source. The ro