BLOCKCHAIN-ENABLED PREDICTIVE AGRICULTURE: ENSURING TRANSPARENCY AND ACCURACY IN AI-BASED CROP YIELD FORECASTING

Abstract

Ensuring transparency and accuracy in crop yield forecasting has become a fundamental requirement for agriculture in the era of climate instability, data fragmentation, and rising global food insecurity. Traditional forecasting frameworks often rely on siloed datasets and centralized data handling, leading to concerns regarding data manipulation, poor traceability, and limited trust among stakeholders. This study proposes an integrated predictive agriculture framework that combines blockchain-based data provenance with advanced artificial intelligence techniques for crop yield prediction. Multisource datasets including agronomic inputs, IoT farm sensors, satellite-derived vegetation indices, meteorological variables, and soil profiles are immutably stored and verified using blockchain smart contracts. AI models utilize these tamper-proof datasets to generate reliable, high-resolution yield predictions. By integrating distributed ledger transparency, machine learning interpretability, and multispectral–climatic data fusion, the framework addresses critical challenges in agricultural monitoring such as data integrity, model accountability, and multi-actor trust. The results highlight that blockchain-enabled AI forecasting significantly improves prediction accuracy, mitigates data manipulation risks, enhances end-to-end traceability, and strengthens farmer and institutional confidence in decision-making. This architecture offers a scalable foundation for agricultural ministries, cooperatives, and global food security agencies to implement transparent, data-driven, and climate-resilient forecasting systems.