The fast growth of smart farming technologies and Internet of Things (IoT) sensors has transformed the agricultural sector, but has brought some fundamental problems, such as data privacy, scalability, and trust in distributed systems. In this paper, AgriChain-FL, a federated blockchain architecture, is introduced and guarantees privacy-preserving, transparent, and energy-efficient collaboration in agricultural ecosystems. The framework integrates federated learning (FL) of decentralized model training with a hybrid Practical Byzantine Fault Tolerance-Proof of Authority (PBFT-PoA) blockchain to allow aggregating data safely, auditing, and participation through incentives. The proposed system was developed based on the SmartFarm Sensor Dataset. Models are trained individually at each local farm node. The hybrid consensus protocol is more scalable, latency-reduced, and uses less energy. The experimental results indicated that AgriChain-FL was more accurate (92.4%), had a throughput of 2350 TPS, and a block latency of 1.0 s, which is also more favorable to analogous frameworks in learning and blockchain performance. Privacy leakage and energy cost were minimized by 73% and 45% respectively. AgriChain-FL is a successful balance between federated intelligence and blockchain trust that provides a privacy-conscious and sustainable smart farming platform. These findings confirm its possibilities as the basis of secure, decentralized, and energy-aware digital agricultural systems.
Amar Y. Jaffar (Sun,) studied this question.