Simulation-Driven Neuro-Symbolic Explainable AI with Blockchain Validation for Precision Agriculture Syed Ibad Ali¹, Shashi Kant Gupta2

Precision Agriculture (PA) systems increasingly employ deep learning for irrigation and nutrient optimization; however, existing models prioritize prediction accuracy while neglecting agronomic feasibility, interpretability, and decision integrity. This paper proposes a simulation-driven neuro-symbolic explainable AI framework integrated with blockchain-based validation. The framework combines multimodal data preprocessing, domain-randomized digital twin environments, Proximal Policy Optimization (PPO) reinforcement learning, a Constraint-Guided Neuro-Symbolic Policy Network (CG-NSPN), SHAP-based explainability, and permissioned blockchain validation using Proof-of-Integrity and Secure Validation (PoISV). The proposed approach embeds symbolic agronomic rules within policy learning to ensure feasible irrigation and fertilizer recommendations under dynamic climatic conditions. Simulation results demonstrate improved robustness, reduced infeasible actions, enhanced interpretability, and tamper-proof auditability suitable for trustworthy smart farming ecosystems.