Blockchain-AI Frameworks for Crop Yield Prediction and Land Registration

Abstract

Sustainable agricultural growth depends simultaneously on two notoriously difficult capabilities: (1) accurate, timely crop yield prediction to guide decisions on inputs, risk, and markets, and (2) trustworthy land administration that secures property rights, reduces disputes, and unlocks credit. This manuscript proposes and evaluates an integrated Blockchain–AI (B-AI) framework that addresses both needs in a single socio-technical architecture. On the analytics side, the framework fuses multi-source data—satellite vegetation indices, weather, soil sensors, and agronomic records—through a temporal deep learning stack (CNN–LSTM/Transformer) to forecast yield at field scale. On the governance side, the framework aligns with ISO 19152 Land Administration Domain Model (LADM) and FAO’s Voluntary Guidelines on the Responsible Governance of Tenure (VGGT), using a permissioned blockchain (Hyperledger Fabric) for tamper-evident land records, verifiable credentials for farmer and parcel identities, and smart contracts for provenance, consented data sharing, and claim adjudication. We detail the end-to-end data flow, on-/off-chain partitioning (IPFS for large files), and privacy preservation via zero-knowledge proofs over model inputs and outputs. A prototype is exercised using three growing seasons of open satellite/weather data and simulated IoT soil telemetry, covering 6,000 plots representative of smallholder conditions. Across five states (synthetic administrative clusters), the model reduces RMSE by 18–34% versus strong tabular baselines (Random Forest, XGBoost), and improves field-level R2R^2R2 from 0.52 (RF) to 0.69 (Transformer). A/B policy simulations show that secure land-title anchoring and data-provenance incentives increase voluntary data contribution by 23–31%, which further lifts yield prediction accuracy by ~6% through richer temporal coverage. The combined system supports practical workflows: digitizing titles, linking parcels to sensor streams, issuing season-specific data-use credentials, and triggering weather-index insurance payouts through auditable smart contracts. We discuss deployment considerations—interoperability, cost, and governance—and outline future work on federated learning across jurisdictions, spatial generalization, and integration with climate-resilience programs.