Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops
arXiv:2607.06585v1 Announce Type: cross 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
The increasing availability of agricultural data and advancements in computer vision and deep learning enable more precise and scalable solutions for crop disease management.
A significant portion of global agricultural yield is lost annually due to crop diseases, impacting food security and economic stability. This technology offers a scalable method to mitigate these losses.
Traditional manual and subjective disease assessment in agriculture can be replaced by automated, pixel-precise, and objective quantification, leading to more efficient crop management.
- · Agriculture tech companies
- · Farmers
- · Food security initiatives
- · AI developers in agriculture
- · Manual crop inspection services
- · Regions dependent on traditional farming methods
Improved crop yield and reduced pesticide use through early and accurate disease detection.
Increased food production efficiency contributes to global food security and potentially stabilizes food prices.
The application of this technology could lead to the development of autonomous agricultural systems that manage entire farm cycles from planting to harvest.
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Read at arXiv cs.LG