A Comprehensive Framework for Remote Sensing and AI-Driven Real-Time Cotton Health Monitoring and Disease Detection

Abstract. Effective monitoring of cotton fields, especially at the regional level, while also detecting diseases in individual plants, remains a significant problem in precision agriculture. This paper presents a combined framework for monitoring cotton in Pakistan, using satellite remote sensing and artificial intelligence-based leaf image classification. Multi-temporal Sentinel-2 imagery from the 2022 kharif season was used to map cotton fields and evaluate canopy condition during the growing season. Cotton fields were mapped using a Random Forest classifier with an overall accuracy of 93% and a Kappa coefficient of 0.82. The estimated cotton acreage of 65,269 ha nearly matched official figures. The crop state inside the mapped cotton area was then evaluated using a Fused Health Index constructed from NDVI, EVI, NDMI, NDRE, and SAVI. The results showed geographic variability in canopy condition, with 24.5% of the region falling into the low-health class, 50.9% in the moderate-health class, and 24.6% in the high-health class. A Vision Transformer model achieves 97% accuracy in classifying RGB images of cotton leaves into eight diseases and conditions. The satellite analysis identifies where stress is concentrated at the district scale, while the image-based model gives symptom-level diagnostic help. Together, these results suggest that combining remote sensing and artificial intelligence can improve timely cotton monitoring and allow more targeted field management.