Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops
The paper proposes a unified deep learning pipeline integrating semantic segmentation, regression-based severity estimation, and disease classification for plant disease severity quantification. On the Apple Tree Leaf Disease Segmentation dataset, U-Net with MobileNetV2 achieves 98.20% pixel accuracy, 0.70 mIoU, and 99.41% detection accuracy at 14.7 ms per image, suitable for real-time use. The computed severity index strongly correlates with expert annotations (r=0.968), demonstrating reliability for automated crop monitoring.
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[Submitted on 5 Jul 2026]
Title:Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops
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Abstract: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.
Comments: 26 pages, 15 figures, 5 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2607.06585 [cs.CV]
(or arXiv:2607.06585v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.06585
arXiv-issued DOI via DataCite
Submission history
From: Raunak Kumar [view email] [v1] Sun, 5 Jul 2026 06:04:20 UTC (2,899 KB)
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