用语义分割量化作物病害严重程度，实现精准农业的实时病害评估。
Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops
- 通过分割病叶面积比例，将病害分为四类严重等级。
- 最佳模型达98.2%像素准确率，14.7毫秒/图，支持实时分析。
- 结果与专家标注高度一致，适合田间自动化监测系统。
植物病害由生物和非生物胁迫引起，每年导致全球农作物产量损失20%-40%，经济损失超2200亿美元。精准且可扩展的胁迫量化对精准农业至关重要，但传统人工评估耗时且主观。本文提出一个统一的深度学习流程，融合语义分割、基于回归的严重度估计与疾病分类。病害严重度依据感染叶面积比例划分为四个等级（低至极高）。在苹果树叶片病害分割数据集（1,641张图像，六类）上测试了四种模型：U-Net（MobileNetV2）、SegFormer、FCN和PSPNet。其中，U-Net+MobileNetV2表现最优，达到98.20%像素准确率、0.70 mIoU和99.41%检测准确率，每幅图像处理仅需14.7毫秒，适合实时应用。SegFormer表现良好（mIoU 0.66），而FCN与PSPNet空间精度较低（约0.49 mIoU）。计算得到的严重度指数与专家标注高度相关（r = 0.968，R² = 0.937），验证了该系统在自动化作物监测与决策支持中的可靠性。
原文摘要 · Abstract (English)
Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually, resulting in economic damages exceeding USD 220 billion. Accurate and scalable stress quantification is essential for precision agriculture, yet traditional manual assessments are labour-intensive and subjective. This paper proposes a unified deep learning pipeline integrating semantic segmentation, regression-based severity estimation, and disease classification. Stress severity is categorised into four levels (Low to Very High) based on the proportion of infected leaf area. Experiments on the Apple Tree Leaf Disease Segmentation dataset (1,641 samples, six classes) evaluate four models: U-Net (MobileNetV2), SegFormer, FCN, and PSPNet. U-Net with MobileNetV2 achieves the best performance with 98.20% pixel accuracy, 0.70 mIoU, and 99.41% detection accuracy at 14.7 ms per image, making it suitable for real-time use. SegFormer performs competitively (mIoU 0.66), while FCN and PSPNet show lower spatial accuracy (approximately 0.49 mIoU). The computed severity index strongly correlates with expert annotations (r = 0.968, R^2 = 0.937), demonstrating the system's reliability for automated crop monitoring and decision support.
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