Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops
Quick Answer
This study introduces a deep learning framework for quantifying disease severity in crops, achieving 98.20% pixel accuracy with U-Net and MobileNetV2.
Quick Take
The system categorizes stress into four levels and correlates strongly with expert assessments, enhancing automated crop monitoring.
Key Points
- U-Net with MobileNetV2 achieves 98.20% pixel accuracy and 99.41% detection accuracy.
- The framework categorizes stress severity into four levels based on infected leaf area.
- SegFormer performs competitively with a mean Intersection over Union (mIoU) of 0.66.
- The severity index shows a strong correlation with expert annotations (r = 0.968).
- Automated monitoring can potentially reduce labor costs in agriculture.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
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