Green Core Intelligence Layer — Markov-Driven Crop Field State Transition Modelling for Autonomous Agricultural Decision Systems and Farmer Behaviour Intelligence

This paper presents the Green Core Intelligence Layer (MGC-Layer), a Markov-driven crop field AI system built on Markov chain logistic regression with three operationally distinct sub-models: MGC-M1 for staple crops (wheat, rice, cotton) in Punjab and Sindh plains; MGC-M2 for perennial crops (sugarcane, banana) in riparian and canal zones; and MGC-M3 for smallholder mixed horticulture plots under two acres. The system models a 7×7 stochastic transition probability matrix across seven crop field states, updating transition probabilities in real time from multispectral drone imagery, weather feeds, and SAR-BioVolume biomass estimates. Green Core delivers actionable behaviour node signals — irrigate, harvest, stress alert — with a 10–14 day advance warning window before visual stress indicators are observable. Simulation results demonstrate state prediction accuracy of 0.97 at 1-day horizon, with agricultural performance improvements of +18% irrigation timing, +24% harvest timing, +31% stress detection, +48% waste capture, and +21% overall yield ratio versus no-AI baseline. Green Core is the intelligence layer of a three-part autonomous agricultural AI stack, receiving sensing inputs from SAR-BioVolume and operating under the safety governance of the KIL veto architecture.