Agriculture has long been essential to human civilizations, providing food, livelihoods and financial strength. As the base of global food security and rural progress, its continued evolution is important to encounter current and upcoming challenges. In recent years, agriculture has met increasing pressure from climate change, unsustainable production practices and persistent inefficiencies within agri-food supply chains. Outdated agricultural methods often struggle to respond effectively to these complex and interrelated challenges, placing food security and farmer livelihoods at risk. In response, emerging digital technologies such as the blockchain, Internet of Things (IoT) and artificial intelligence (AI) offer new opportunities to support sustainable, transparent and climate resilient agricultural systems. Despite their potential, the integrated application of these technologies in agriculture remains limited and fragmented. This doctoral research addresses this gap through a structured, multi-phase investigation into blockchain-IoT-AI systems for Climate Smart Agriculture (CSA). The study begins with a bibliometric analysis of peer reviewed literature published between 2016 and 2023, revealing a strong upward trend in research activity with an average annual growth rate of approximately 47.6%. However, the analysis also highlights that a large proportion of existing studies focus primarily on technical aspects, while issues related to scalability, economic feasibility and user adoption remain underexplored. Building on these insights, the thesis advances toward applied research through a series of case based studies. The Italian tomato processing sector, valued at approximately €3 billion annually, serves as a primary application context. A blockchain based supply chain model was developed and tested under laboratory conditions. The results demonstrate improved product traceability, measurable reductions in post-harvest losses and the potential for fairer pricing mechanisms that strengthen farmer’s market positions. This work is further extended through the development of a blockchain-IoT decentralized application (BIoT-DApp), which integrates smart contracts, sensor data and user interfaces to manage transactions, environmental monitoring and logistics. Test net deployments achieved 100% transaction execution success, with core operations maintaining gas costs consistently, indicating both technical feasibility and scalability. The final phase of the research applies the integrated framework to high value and fraud prone crops, with a focus on saffron production. By combining computer vision (CV) techniques with IoT sensing and blockchain based data management, the system supports automated quality assessment and fraud detection. The proposed approach achieved an anomaly detection accuracy of approximately 98%, while ensuring secure and tamper proof traceability across the entire production i chain. Together, these case studies demonstrate a scalable, data driven and farmer centric digital framework capable of improving transparency, enhancing pricing equity, strengthening market access and supporting broader climate resilience objectives. While challenges remain, including blockchain transaction costs, digital infrastructure gaps in rural areas and varying levels of technological literacy, the findings indicate that emerging solutions such as Layer-2 blockchain architectures and edge computing can help mitigate these constraints. Collectively, this research contributes a practical and evidence based framework for digitally enabled agriculture, advancing a model that combines technological innovation with real world agricultural needs. By aligning efficiency, transparency and inclusiveness, the thesis lays a strong foundation for future agri-food systems that are resilient, equitable and responsive to the demands of the twenty first century.
Blockchain-IoT Integration for Smart and Sustainable Agriculture: A Multi-Domain Approach from Bibliometric Insights to Practical Applications
SAFEER, SAJID
2026-06-23
Abstract
Agriculture has long been essential to human civilizations, providing food, livelihoods and financial strength. As the base of global food security and rural progress, its continued evolution is important to encounter current and upcoming challenges. In recent years, agriculture has met increasing pressure from climate change, unsustainable production practices and persistent inefficiencies within agri-food supply chains. Outdated agricultural methods often struggle to respond effectively to these complex and interrelated challenges, placing food security and farmer livelihoods at risk. In response, emerging digital technologies such as the blockchain, Internet of Things (IoT) and artificial intelligence (AI) offer new opportunities to support sustainable, transparent and climate resilient agricultural systems. Despite their potential, the integrated application of these technologies in agriculture remains limited and fragmented. This doctoral research addresses this gap through a structured, multi-phase investigation into blockchain-IoT-AI systems for Climate Smart Agriculture (CSA). The study begins with a bibliometric analysis of peer reviewed literature published between 2016 and 2023, revealing a strong upward trend in research activity with an average annual growth rate of approximately 47.6%. However, the analysis also highlights that a large proportion of existing studies focus primarily on technical aspects, while issues related to scalability, economic feasibility and user adoption remain underexplored. Building on these insights, the thesis advances toward applied research through a series of case based studies. The Italian tomato processing sector, valued at approximately €3 billion annually, serves as a primary application context. A blockchain based supply chain model was developed and tested under laboratory conditions. The results demonstrate improved product traceability, measurable reductions in post-harvest losses and the potential for fairer pricing mechanisms that strengthen farmer’s market positions. This work is further extended through the development of a blockchain-IoT decentralized application (BIoT-DApp), which integrates smart contracts, sensor data and user interfaces to manage transactions, environmental monitoring and logistics. Test net deployments achieved 100% transaction execution success, with core operations maintaining gas costs consistently, indicating both technical feasibility and scalability. The final phase of the research applies the integrated framework to high value and fraud prone crops, with a focus on saffron production. By combining computer vision (CV) techniques with IoT sensing and blockchain based data management, the system supports automated quality assessment and fraud detection. The proposed approach achieved an anomaly detection accuracy of approximately 98%, while ensuring secure and tamper proof traceability across the entire production i chain. Together, these case studies demonstrate a scalable, data driven and farmer centric digital framework capable of improving transparency, enhancing pricing equity, strengthening market access and supporting broader climate resilience objectives. While challenges remain, including blockchain transaction costs, digital infrastructure gaps in rural areas and varying levels of technological literacy, the findings indicate that emerging solutions such as Layer-2 blockchain architectures and edge computing can help mitigate these constraints. Collectively, this research contributes a practical and evidence based framework for digitally enabled agriculture, advancing a model that combines technological innovation with real world agricultural needs. By aligning efficiency, transparency and inclusiveness, the thesis lays a strong foundation for future agri-food systems that are resilient, equitable and responsive to the demands of the twenty first century.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
