AI and blockchain join forces to cut crop losses for small farmers

Smallholder farmers across climate‑vulnerable regions are facing a convergence of risks that traditional agricultural systems are increasingly unable to manage. A new interdisciplinary study presents a data‑driven framework that seeks to address these challenges by tightly integrating artificial intelligence, blockchain, and Internet of Things technologies into what the authors describe as Smart Agriculture 5.0.

Published in Frontiers in Blockchain, the study Smart Agriculture 5.0: Blockchain and Reinforcement Learning Synergy for Multicropping Optimization and Traceable IoT‑Enabled Supply Chains, the study proposes a shift away from sensor‑only precision farming toward uncertainty‑aware, decentralized, and farmer‑centric decision systems.

Based on real‑world data collected from smallholder farms in Andhra Pradesh, India, the research provides empirical evidence that advanced AI models combined with blockchain infrastructure can significantly reduce crop losses while improving trust and efficiency across agricultural supply chains.

AI-driven multicropping decisions under uncertainty

The study introduces a hybrid artificial intelligence model designed to tackle one of agriculture’s most persistent problems: uncertainty. Multicropping systems involve simultaneous cultivation of multiple crops, each responding differently to soil conditions, rainfall, pests, and market demand. Conventional regression models often fail to capture this complexity, particularly when data are incomplete or environmental variables fluctuate unpredictably.

To address this, the authors combine Neutrosophic multi‑regression with reinforcement learning. Neutrosophic logic allows the model to represent uncertainty explicitly, distinguishing between known information, indeterminate factors, and conflicting signals. This is particularly relevant in smallholder contexts, where sensor coverage may be uneven and historical data limited. Reinforcement learning is then used to optimize model parameters dynamically, enabling the system to learn from feedback and improve predictions over time.

The model was trained and evaluated using data collected between 2022 and 2024 from farms cultivating rice, banana, turmeric, coconut, cocoa, and elephant foot yam. These crops were selected to reflect diverse growth cycles, climate sensitivities, and economic profiles. According to the study, the hybrid approach consistently outperformed traditional regression techniques, achieving substantially lower prediction errors and enabling more accurate forecasts of crop loss.

In practical terms, the improved predictions translate into earlier risk detection and better‑timed interventions. Farmers can adjust planting schedules, input use, or harvesting strategies based on probabilistic loss estimates rather than reactive observation. The authors report that this approach enables a projected reduction in crop losses of 25 to 35 percent, a significant margin in regions where even small yield declines can threaten livelihoods.

Importantly, the study frames AI not as a replacement for farmer expertise but as a decision support layer. The system is designed to augment human judgment by presenting uncertainty‑aware recommendations, allowing farmers to weigh risks rather than follow opaque algorithmic outputs. This emphasis aligns with the broader Agriculture 5.0 paradigm, which prioritizes human–AI collaboration over automation for its own sake.

Blockchain-enabled transparency and trust in supply chains

While predictive accuracy is essential, the authors argue that intelligence alone is insufficient in agricultural systems characterized by weak institutional trust and fragmented markets. Smallholder farmers often face delayed payments, price manipulation, and lack of transparency once crops leave the farm. To address this, the study integrates a blockchain layer that records predictions, transactions, and IoT‑generated data in an immutable ledger.

The proposed framework uses blockchain to store crop loss predictions, farmer identities, and transaction records securely. Smart contracts automate payments when predefined conditions are met, such as loss thresholds or delivery confirmations. By eliminating intermediaries and reducing manual verification, the system aims to lower transaction costs while increasing accountability.

IoT devices feed real‑time environmental and crop data into the system, ensuring that blockchain records reflect on‑farm conditions rather than post‑hoc reporting. This integration enables end‑to‑end traceability, allowing buyers, insurers, and regulators to verify production conditions and risk assessments without relying on centralized authorities.

The study reports that this decentralized architecture significantly reduces operational overhead compared with traditional centralized databases. By minimizing third‑party involvement and automating verification, transaction costs can be reduced by up to 85 percent. For smallholder farmers operating on thin margins, these savings can be as consequential as yield improvements.

Beyond efficiency, the blockchain component addresses a structural trust deficit in agricultural markets. Immutable records reduce disputes over quality, quantity, and timing, while transparent smart contracts provide clearer expectations for all parties. The authors position this as a critical step toward fairer market participation for smallholders, who are often excluded from formal financial and insurance systems due to lack of verifiable data.

From Agriculture 4.0 to Agriculture 5.0

Agriculture 4.0 emphasized precision farming through sensors, automation, and data analytics. While these advances improved efficiency, they often treated farmers as passive data sources and overlooked social, economic, and ethical dimensions.

Agriculture 5.0, as articulated in the paper, represents a shift toward systems that are not only intelligent but also resilient, inclusive, and transparent. The integration of uncertainty‑aware AI with decentralized blockchain governance reflects this shift. Rather than optimizing isolated variables, the framework addresses the interconnected nature of agricultural risk, decision‑making, and market coordination.

The authors argue that this approach is particularly relevant for climate‑exposed regions, where uncertainty is the norm rather than the exception. Traditional optimization models assume stable conditions and complete information, assumptions increasingly at odds with reality. By embedding uncertainty directly into predictions and linking them to transparent market mechanisms, the system is designed to function under stress rather than only in ideal conditions.

The study also highlights policy implications. Governments investing in digital agriculture infrastructure often focus on hardware deployment, such as sensors and connectivity. The findings suggest that equal attention should be paid to data governance, transparency, and decision frameworks that empower farmers rather than extract value from them.

The authors acknowledge limitations too. The current implementation is based on a specific geographic region and a limited set of crops. Blockchain deployment was tested in controlled environments rather than at national scale. The authors call for future work involving multi‑region validation, federated learning to protect data privacy, and integration with national agricultural databases.