AI-Driven Weather Forecast Integration with Blockchain Smart Farming Contracts

Abstract

Smallholder and commercial farms alike are increasingly exposed to weather volatility and market uncertainty. Recent progress in data-driven weather prediction and distributed ledgers enables a new class of “smart farming contracts” that can automatically enact agronomic actions or financial transfers when meteorological conditions cross predefined thresholds. This manuscript proposes and analyzes an AI–blockchain architecture that ingests multi-source forecasts (global numerical weather prediction, regional reanalyses, satellite observations, and on-farm IoT), calibrates them with machine learning for local bias correction, quantifies uncertainty, and delivers signed forecast products to on-chain contracts via decentralized oracles. These contracts schedule irrigation, pesticide and fertilizer windows, harvest logistics, energy usage, and parametric risk transfers (e.g., drought or heat payouts) with verifiable audit trails. We develop a methodology for ensemble model fusion (e.g., gradient-boosted or temporal-fusion networks) and probabilistic post-processing to produce location-specific forecast distributions at daily to hourly horizons. A simulated study spanning three agro-climatic zones evaluates reductions in irrigation water (–12–18%), improvements in forecast RMSE (–22–35% vs. raw models), faster and less-contested insurance settlements (from weeks to hours), and modest yield gains (2–6%) associated with better timing of operations. A statistical analysis illustrates the sensitivity of outcomes to forecast skill, oracle latency, and contract design. We discuss governance, data provenance, privacy-preserving oracles, and compliance for both permissionless and permissioned ledgers. Results indicate that coupling AI-calibrated forecasts with verifiable, automation-ready smart contracts can convert climate uncertainty into programmable risk, advancing resilient, sustainable agriculture.