A Simulation-Driven Neuro-Symbolic Explainable AI Framework with Blockchain-Enabled Validation for Trustworthy Precision Agriculture

Artificial intelligence (AI) is increasingly being used in precision agriculture; however, predictive accuracy alone is insufficient to ensure trustworthy agricultural decision-making. Dynamic field conditions, noisy and incomplete sensor data, agronomic constraints, and weakly connected post-hoc explanations can limit the reliability of AI-based recommendations. To address these challenges, this paper proposes PoISV-NSDT, a Proof-of-Integrity Smart Validation and Neuro-Symbolic Digital Twin framework that integrates multimodal data processing, a physics-informed procedural digital twin, reinforcement learning, constraint-guided neuro-symbolic reasoning, explainable AI, and permissioned blockchain validation. The framework initially performs temporal and contextual integration of soil, weather, crop, multispectral, and telemetry data using z-score normalization, similarity-aware KNN imputation, and stress-aware synthetic data enrichment. A domain-randomized Unity ML-Agents digital twin subsequently supports Proximal Policy Optimization (PPO) under diverse soil, crop, and weather conditions. The learned policy is integrated with a three-layer constraint-guided neuro-symbolic policy network that combines Temporal Fusion Transformer (TFT)-based temporal perception, lightweight temporal convolution for imagery, symbolic agronomic rules, knowledge-graph constraints, and an Arctic Puffin Optimization (APO) action tuner. For interpretability, SHAP-based feature attributions are combined with symbolic rule traces to generate a unified explanation of each decision. A permissioned blockchain further records cryptographic hashes associated with input data, model versions, outputs, and explanations to support auditability and provenance. The reported experimental study uses 1,200 season-level episodes with an 80:20 training-to-testing split. The framework reports an R² of 0.919, 2% agronomic rule violations, 92% robustness under sensor noise, 89.7% explanation fidelity under stress, and blockchain throughput of 82 transactions per second with latency below 60 seconds. It also reports an 18% increase in yield, 11% reduction in water consumption, and 7.5% reduction in fertilizer usage. Overall, the proposed framework establishes a system-level approach to trustworthy precision agriculture by integrating prediction, simulation, constraint enforcement, explainability, and verifiable provenance within a unified architecture.