Design of an Iterative Learning Framework for Causal, Risk-Aware, and Validated Crop Health Optimization using Multimodal Agricultural Intelligence Sets

The need for reliable, proven frameworks that can track crop health and iteratively enhance decision-making towards sustainable yield improvement has increased due to precision agriculture's rapid development. The majority of current methods rely on static correlation models that ignore plant physiological limitations, the impacts of causal interventions on plants, adaptive experimentation, and systematic validation. This results in poor generalizability and suboptimal resource efficiency. In order to close these gaps, we create a five-stage iterative learning system that transforms agricultural raw multimodal data into proven, practical crop health optimization plans. PhytoSieve-SSL, the first stage, uses physics-gated self-supervision to combine satellite, UAV, hyperspectral, and soil sensor data to learn uncertainty-calibrated crop health latents. These latents are used in Stage 2, InterveneGraph-NODE, to create a causal spatiotemporal intervention graph that models counterfactual health trajectories by incorporating the Neural ODE dynamics. Subject to operational limitations, Stage 3, B-FLEX, maximizes value-of-information through low-regret exploration and budgeted field testing. Risk-aware model-based reinforcement learning is used in Stage-4, RIPA, to derive prescriptive rules that maximize profit while utilizing resources efficiently. Stage 5, COSA-Guard, ensures long-term dependability and governance through in-line and ongoing validation of the Crop Health Set (CHS) by conducting online counterfactual surveillance and conformal drift repair.