Learning Causal Agroecosystem Dynamics through Physics-Guided Machine Intelligence

Mahule Roy, Subhas Roy

Abstract:

This paper presents a comprehensive hybrid causal AI framework that integrates physics-based agroecosystem models with data-driven causal discovery to address the limitations of correlation-based agricultural intelligence. Our approach, Hybrid Causal Discovery (HCD), combines structural causal models with physical constraints from domain knowledge, enabling robust causal graph inference from multimodal agricultural data. Through extensive evaluation across three diverse agricultural datasets spanning 5 years and 142 fields,
we demonstrate superior performance in prediction accuracy (23.6% improvement in RMSE), robustness to distribution shifts (47.3% better OOD generalization), and causal interpretability. We provide theoretical guarantees for causal identifiability, comprehensive uncertainty quantification, and address key technical challenges including temporal causal discovery, adversarial robustness, multi-scale modeling, and real-world deployment considerations. Statistical analysis confirms significant improvements (p < 0.01) across all metrics, while ablation studies validate the importance of each component. Our framework represents a significant advancement towards explainable, robust, and trustworthy agricultural intelligence.