AI4Carbon: Smart Image-Analytics Platform for Monitoring Residue Retention Levels in Rice Production Systems – Automated Visual Quantification - Sowjanya, Palle Sai; Patil, Mukund; Rupavatharam, Srikanth; Gogumalla, Pranuthi; Choudhari, Pushpajeet L.; Dihudi, Munmun (International Crops Research Institute for the Semi-Arid Tropics, 2025)
Vision2Biomass is an AI-driven approach for quantifying crop residue retention in paddy fields to support digital Monitoring, Reporting, and Verification (MRV) systems for agricultural carbon accounting. The probe addresses limitations of manual residue assessment, which is labor-intensive, subjective, and difficult to scale. Using geo-tagged field photographs, computer vision models segment residue, soil, and vegetation at the pixel level and estimate residue retention categories. By combining image segmentation with deep feature extraction and classification, the system provides consistent, interpretable residue estimates that can be directly linked to carbon models. Vision2Biomass demonstrates the potential of scalable, image-based analytics for climate-smart agriculture and verifiable carbon mitigation.