Blockchain-Assisted Modular and Scalable Precision Agriculture System With Internet of Thing Systems
Bezawada Manasa, Venkata Krishna Parimala, M. Pounambal, Vishnupriya S Devarajulu · Cureus Journal of Computer Science. · 2025
The demanding challenges like data privacy, scalability and real-time decision-making can be addressed with an influential method by integrating federated learning and blockchain to make agricultural system to be smarter. These serious challenges are addressed in this paper by proposing a novel framework named AgriTrustChain++. The proposed method is a hybrid method and an integration of smart contract automation, federated edge intelligence and permissioned blockchain. There are five layers in the proposed framework, which include application layer, smart contract layer, blockchain layer, federated intelligence layer and IoT edge layer. The proposed system performs local computations securely and also preserves the end-to-end traceability with faster decision-making capability. TensorFlow Federated and Hyperledger are used in the implementation. A total of 3,000 records are used in the simulation process, where IoT and edge devices over 15 crop cycles are considered. The proposed system is evaluated and exhibits a throughput of 110 records/s, 27% energy conservation, 92.5% accuracy and 1.8 s average latency. The performance of the proposed system, AgriTrustChain++, is compared with AgriFusion and AgriLedger and is shown to be outperforming.