SAR-BioVolume — Cross-Spectral Radar Fusion Pipeline for Crop Biomass Estimation and Agricultural Waste Intelligence in Smallholder Farm Environments

This paper presents SAR-BioVolume, a cross-spectral synthetic aperture radar fusion pipeline that estimates standing crop biomass volume through dual-polarisation coherency matrix decomposition, eigenvalue-based biomass inversion, and Bayesian plot-level verification at sub-2-acre smallholder resolution. Grounded in Cloude-Pottier decomposition theory and linear algebra-driven tensor fusion, SAR-BioVolume achieves biomass estimation accuracy of 0.89–0.96 across all atmospheric conditions tested, including heavy monsoon rain (+79pp over optical baseline), night operations (+87pp), and dust storm conditions (+71pp) , where optical sensing accuracy collapses to 0.05–0.18. The paper introduces a novel crop waste intelligence module that identifies, quantifies, and timestamps the biomass value of five agricultural waste streams, banana stem, sugarcane bagasse, cotton stalks, rice husks, and wheat straw, using an Arrhenius kinetic decay model to predict feedstock quality degradation and collection deadlines for industrial offtakers. To the best of the author's knowledge, no prior published system integrates SAR-based biomass sensing with an Arrhenius-kinetics quality degradation model for agricultural waste stream monetisation at sub-2-acre plot resolution. SAR-BioVolume operates as the sensing foundation of a three-layer autonomous agricultural AI stack, feeding biomass and entropy-anisotropy parameters to the Green Core Intelligence Layer and real-time crop state data to the KIL safety veto architecture. Initial findings are simulation-based; physical deployment and ground-truth calibration are planned as the next research stage.