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<p>To enhance precision farming, optimize resource utilization, and ensure transparency in agricultural supply chains, this paper presents a hybrid framework integrating blockchain, artificial intelligence (AI), and Internet of Things (IoT). The proposed system, SmartOrgin 2.0, collects real-time data on crucial parameters including soil moisture, temperature, humidity, and crop health via IoT sensors, which transmit data over LoRa networks effective within a 100 KM range. The system addresses challenges such as rising food demand, water shortages, and climate impacts. AI models—ensemble learning and neural networks—analyze this data to forecast crop yields, detect disease onset, and optimize irrigation. Blockchain technology, implemented via Hyperledger Fabric with Java-based smart contracts, ensures tamper-proof, decentralized recording of farm activities, enhancing trust and traceability. Our architecture incorporates renewable energy sources to support off-grid sensor and node operations. Both simulation-based pilots and real farm data from a chili farm in Southern India were used for validation. Quantitative analysis shows 91% prediction accuracy, 24% reduction in irrigation water usage, and ~ 2.1s blockchain latency with scalability up to 300 nodes per gateway. A cost-benefit analysis highlights viability for sustainable deployment.</p>

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A hybrid framework for integrating IoT, AI and blockchain towards sustainable smart agriculture

  • 期刊论文

  • 发表日期：

    2026年3月4日

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  • Rathiya R,
  • S. Rajalakshmi,
  • A. S. Shanthi

摘要

To enhance precision farming, optimize resource utilization, and ensure transparency in agricultural supply chains, this paper presents a hybrid framework integrating blockchain, artificial intelligence (AI), and Internet of Things (IoT). The proposed system, SmartOrgin 2.0, collects real-time data on crucial parameters including soil moisture, temperature, humidity, and crop health via IoT sensors, which transmit data over LoRa networks effective within a 100 KM range. The system addresses challenges such as rising food demand, water shortages, and climate impacts. AI models—ensemble learning and neural networks—analyze this data to forecast crop yields, detect disease onset, and optimize irrigation. Blockchain technology, implemented via Hyperledger Fabric with Java-based smart contracts, ensures tamper-proof, decentralized recording of farm activities, enhancing trust and traceability. Our architecture incorporates renewable energy sources to support off-grid sensor and node operations. Both simulation-based pilots and real farm data from a chili farm in Southern India were used for validation. Quantitative analysis shows 91% prediction accuracy, 24% reduction in irrigation water usage, and ~ 2.1s blockchain latency with scalability up to 300 nodes per gateway. A cost-benefit analysis highlights viability for sustainable deployment.