像素精确的可解释压力索引：一种用于田间作物病害严重程度量化的语义分割框架
Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops
AI总结：
本文针对植物病害致农业产量损失大的问题，提出集成语义分割等的深度学习管道，基于感染叶面积比例分级病害严重程度，在苹果树叶病害数据集上实验，U-Net（MobileNetV2）性能最佳，严重程度指数与专家注释相关性高，可用于作物自动监测和决策支持。
摘要：
植物病害由生物和非生物胁迫引起，每年导致全球农业产量损失约20%-40%，经济损失超2200亿美元。准确且可扩展的压力量化对精准农业至关重要，但传统人工评估劳动强度大且主观。本文提出一个统一的深度学习管道，集成语义分割、基于回归的严重程度估计和病害分类。基于感染叶面积比例将压力严重程度分为四个等级。在苹果树叶病害分割数据集上对四个模型进行实验，U-Net（MobileNetV2）性能最佳，计算出的严重程度指数与专家注释高度相关，证明了该系统对作物自动监测和决策支持的可靠性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
