This chapter presents the concept of a Smart Rice Mill as an intelligent, connected, automated, and traceable rice-processing ecosystem. It integrates IoT sensors, computer vision, deep learning, Edge AI, cloud analytics, predictive maintenance, intelligent control, and blockchain to improve rice-processing operations. The chapter discusses automated grain inspection, variety classification, defect detection, broken-rice estimation, milling-quality prediction, machine monitoring, process optimization, and digital recording of batch history. It also examines implementation challenges involving legacy machinery, hardware and sensor reliability, cybersecurity, staff training, integration, and economic feasibility. The proposed future direction is a closed-loop Smart Rice Mill capable of sensing paddy and machine conditions, predicting quality, adjusting processing parameters, verifying output, and maintaining complete traceability.
This chapter proposes an integrated Blockchain–IoT–AI framework for secure and intelligent quality traceability, particularly in agricultural and rice supply chains. It explains how IoT sensors can continuously collect physical and environmental information, AI models can analyze images and sensor data for quality assessment, and blockchain can securely record important quality events and processing information. The framework supports unique digital identities for rice batches, quality monitoring, defect detection, moisture estimation, quality scoring, and QR-based access to traceability information. The chapter examines applications in rice quality certification, smart rice mills, food safety, warehouses, export-quality monitoring, consumer verification, and government procurement. Challenges related to data quality, sensor reliability, interoperability, stakeholder participation, scalability, and regulatory coordination are also addressed.
Abstract This study explores the role of Artificial Intelligence (AI) in transforming agricultural supply chain management in Bangladesh through a systematic comparative analysis of existing literature, institutional reports, and global case studies. AI technologies including predictive analytics, machine learning, blockchain, and precision agriculture are examined for their potential to address longstanding inefficiencies in Bangladesh’s agri-supply chain. The study finds that AI-driven demand forecasting models using LSTM and ARIMA achieved 89–92% crop yield prediction accuracy, representing a 37% improvement over traditional methods. Smart warehousing systems reduced operational costs by 25% and increased order processing speed by 40%, while blockchain integration cut payment cycles from 15 days to 2.3 days and increased smallholder farmer incomes by 22–25%. Precision agriculture technologies achieved 25% yield growth with 15–20% water savings and 30% fertilizer efficiency gains. Despite these promising outcomes, Bangladesh’s AI adoption rate remains at only 18%, significantly behind India (35%) and Vietnam (28%), primarily due to insufficient infrastructure, lack of digital literacy, and high implementation costs. The study proposes targeted policy interventions including IoT subsidies, farmer training programs, and public-private partnerships to enable inclusive and sustainable AI integration across Bangladesh’s agricultural sector.
Abstract A secure platform for exchanging and storing agricultural data is provided via a blockchain-powered framework. By integrating edge computing, blockchain technology, and the Internet of Things (IoT) the production of crops can be boosted while using fewer natural resources. In the sector of agriculture, sensors and equipment gather various data about the landscape, which can subsequently be delivered to a server in a cloud environment. Due to its extreme fragility, these data must be securely stored and guarded from unwanted access. The core aim of this work is to propose a hybrid Reconditioned Random value-based Wombat Optimization with Adaptive Multi-scale Vision Transformer-based EfficientNet (RRWO-AMViT-ENet) model integrated with Ethereum smart contracts for secure pest detection and smart irrigation in IoT environments. The gathered agricultural images are initially stored and managed using the Ethereum blockchain. Then, node authentication is performed using the Smart Contract-based Adaptive Deep Support Vector Machine (SC-ADSVM). A Reconditioned Random value-based Wombat Optimization (RRWO) is utilized to optimize the variables of the developed SC-ADSVM. In order to perform pest detection and smart irrigation, the Adaptive Multi-scale Vision Transformer-based EfficientNet (AMViT-ENet) is used. The proposed model is implemented on the IP102-Dataset, where it obtained an accuracy of 96.39% in the pest detection operation. Thus, the proposed model provides effective results for pest detection and the smart irrigation process. From the attained results, it is concluded that the recommended strategy can provide intelligent service to the farmer.
The global dairy industry confronts a persistent structural challenge in operationalising food safety and animal welfare compliance. Manual inspection regimes and intermittent audits are demonstrably inadequate for the heterogeneous, geographically dispersed landscape of small-scale farming, where data integrity, real-time monitoring capability, and regulatory transparency are simultaneously compromised. This article presents GreenDairyChain, an integrated compliance innovation framework that synthesises four enabling technologies: GreenEdgeML (a lightweight TinyML inference engine optimised for microcontroller-class devices), Privacy-Preserving Federated Learning (FL) with Graph Attention Network (GAT)-based dynamic clustering, Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) for cryptographic compliance verification, and a Layer-2 Polygon zkEVM Blockchain with domain-specific smart contracts governing farm identity, violation detection, audit triggers, and licence management. GreenEdgeML executes multimodal sensor fusion across four signal modalities (body temperature, accelerometer activity, ammonia concentration, and milk pH) entirely on-device using 8-bit integer quantisation, consuming 64.6 KB RAM and 82.7 mW per inference cycle on the ESP32 platform. The FL engine employs GAT-based farm clustering with DBSCAN outlier exclusion to address non-IID data heterogeneity while maintaining Byzantine fault resilience. Compliance inferences are encoded as R1CS arithmetic circuits (14,240 constraints) and verified on-chain at O(1) cost through ZK-SNARK proofs generated in 1.25 seconds. Evaluated on the Shahhet28121 benchmark dataset across 16 biomarkers, the full system achieves 96.94% global classification accuracy, a 97.7% reduction in per-round communication payload (4.25 KB), and maintains classification accuracy above 90% under 20% Gaussian sensor noise. Ablation experiments confirm that each architectural component contributes independently to system performance. The findings carry implications for green business innovation, sustainable agriculture governance, and the design of trustworthy AI ecosystems in resource-constrained rural contexts.
This research discusses the incorporation of IoT with blockchain technique to enhance the efficiency of smart farming systems, particularly focusing on plant disease classification, pest detection, and smart irrigation. The study aims to develop a secure and effective IoT-based smart farming framework using the Ethereum blockchain to store and transmit data, and a Hybrid Convolution Adaptive Recurrent MobileNet (HC-ARMNet) model for predictive analytics, optimized by the Improved Secretary Bird Optimization (ISBO) algorithm. The research employs IoT sensors to acquire real-time data, which is then stored in the Ethereum blockchain to ensure security. The HC-ARMNet model, combining 1D/2D convolutions with recurrent connections, processes this data for pest detection and irrigation management. The ISBO algorithm is leveraged to fine-tune the technique's parameters. Datasets used: The proposed system utilizes three standard datasets for evaluation. The PlantifyDr Dataset is used for classifying plant disease, and the Pest Detection Dataset is used for recognizing pests. Also, for the smart irrigation process, the significant field images are collected manually. The accuracy, precision, and FNR rates of the ISBO-HC-ARMNet-aided plant disease classification are 94.16%, 94.2% and 5.87%. At the same time, the ISBO-HC-ARMNet-based pest detection process's accuracy, sensitivity, and specificity are 93.78%, 93.79% and 93.76%, respectively. In addition, the ISBO-HC-ARMNet-based smart irrigation task's MSE is 3.21, SMAPE is 0.03, and MASE is 30.23. Thus, the designed system showcases promising performance over classical approaches in terms of accuracy and error rates for plant disease classification, pest detection, and smart irrigation. The research concludes that the IoT-aided smart farming framework with blockchain and the HC-ARMNet model provides a robust solution for secure and efficient agricultural management. The system's predictive capabilities provide accurate and timely data analysis, facilitating to the improvement of precision agriculture. Future work will focus on improving the system with advanced feature extraction strategies to reduce processing time.
Abstract Food security and the stakeholders’ trust are essential to ensure that agricultural supply chains are transparent and secure. This research presents a Queueing-Assisted Blockchain Smart Contract (QABSC) framework to enhance end-to-end traceability in the millet supply chain. The framework incorporates fog computing into real-time data processing to reduce latency and optimizes transaction flow using queueing techniques, thereby ensuring an efficient and scalable blockchain supply chain platform. The Internet of Vehicles and Things (IoVT) connects cars, sensors, roadside infrastructure, and cloud and edge technologies to make transportation and mobility smarter. By integrating fog-layer intelligence with blockchain-based immutable record-keeping, Internet of Vehicles and Things enabled sensing and vehicular logistics, and end-to-end visibility, the proposed system may ensure tamper-resistant monitoring of millet products from farms to customers. Internet of Things (IoT) sensors collect real-time information about millet quality and storage conditions. This data is securely stored using the InterPlanetary File System (IPFS) and verified by smart contracts on a distributed ledger. This approach ensures automated compliance verification for auditors and regulators, immutable data storage, and conditional privacy. The proposed model reduces bottlenecks in blockchain transaction processing and enhances efficiency, privacy, data integrity, and trust among producers, distributors, retailers, farmers, and buyers. The proposed model is evaluated based on key performance metrics. The experimental evaluations of the proposed framework demonstrate enhanced throughput, improved transparency, reduced computational overhead, and robust security. This research focuses on a unique integration of smart contracts, queueing theory, IPFS, Fog Computing, IoT devices, and blockchain technology to promote sustainable and transparent millet supply chain management.
This technical paper presents the architectural foundations and technological innovations of the GEORGIA platform. The publication introduces a decentralized, cloud-native cyber-physical system that integrates biophysical modelling, Explainable Artificial Intelligence, Zero-Trust Federated Learning, and Distributed Ledger Technology to support precision irrigation and drought resilience across diverse European agricultural systems.
Agricultural trade in developing countries continues to depend on informal agreements, multi-tier intermediary networks, and centralized payment mechanisms, resulting in payment delays of 30–90 days, information asymmetry, and weakened bargaining power for smallholder farmers. This paper presents AgriHandshake, a blockchain-based smart contract platform enabling direct crop trading between farmers and vendors through an automated escrow payment mechanism deployed on the Ethereum network. The system employs a hybrid architecture that stores cryptographic state hashes and escrow logic onchain while offloading trade metadata and delivery documentation to the InterPlanetary File System (IPFS), reducing average transaction gas costs to approximately 85,000–210,000 gas units per operation. A Solidity-based escrow contract enforces a structured four-state machine (CREATED→FUNDED→DELIVERED→COMPLETED/DISPUTED) with a 72-hour automatic payment-release timer implemented via block.timestamp. Experimental evaluation on the Ethereum Sepolia testnet demonstrates average smart contract function execution latency under 15 seconds, end-to-end trade confirmation within 3–8 minutes including IPFS upload, and strong resistance to reentrancy and front-running attacks. Comparative analysis against eNAM, FarMarket, and AgriOnBlock confirms that AgriHandshake is the first platform to combine a dedicated escrow payment guarantee, decentralized off-chain storage, and a farmer-centric usability model within a single deployable framework
Huda M. Elmatsani, Arief Sartono, S. Joni Munarso, Sari Intan Kailaku · 14 authors
Background Agricultural supply chains are characterized by high transaction costs and agency risks stemming from information asymmetry and biological variability. Although blockchain is widely proposed as a solution, existing literature predominantly focuses on passive traceability rather than active algorithmic governance. Methods This study conducts a bibliometric synthesis of 367 documents (2018–2025) to map the field’s intellectual structure and research orientation. Co-occurrence analysis was employed to reveal distinct thematic clusters and identify the evolution of technological infrastructure in the sector. Results The analysis reveals a critical volume-impact paradox within the technological infrastructure group and a 16:1 asymmetry between traceability and automation research. This indicates a significant gap in leveraging smart contracts for economic enforcement and active supply chain management. Conclusion We propose the Agri-Cognito framework, a prescriptive architecture designed to bridge the cognitive void through AI-driven pre-consensus validation. The framework provides a theoretical blueprint for transitioning agricultural blockchains from passive digital logbooks to autonomous governance ecosystems, offering a direct response to the “oracle problem” and structural inefficiencies in current implementations.
Abstract Decentralized Web Applications (dApps) built on blockchain, Web3 technologies, and the Inter- Planetary File System (IPFS) are emerging as a promising solution to longstanding challenges in agriculture. Conventional centralized systems often result in opaque supply chains, data tampering, fraud, and limited empowerment of smallholder farmers. This systematic review identifies and analyzes 12 representative studies published between 2017 and 2025, selected via a structured search across IEEE Xplore, Scopus, and Google Scholar using a defined inclusion and exclusion protocol. Studies are examined with particular emphasis on supply-chain traceability, IoT-enabled smart farming, parametric crop insurance, direct farmer-to-buyer marketplaces, and secure farm-data management. Most implementations leverage Ethereum smart contracts or Hyperledger Fabric, integrate IoT sensors for real-time monitoring, and employ IPFS for off-chain storage of large files such as sensor readings and images. Key benefits include immutable records that prevent tampering, end-to-end traceability for rapid identification of contaminated produce, automatic smart-contract payments, and trust-building without intermediaries. Notable examples are the Walmart-IBM blockchain pilot for mango and pork traceability and platforms such as Etherisc and Arbol for parametric crop insurance. However, challenges remain, including high gas fees and slow transaction speeds on public blockchains, high energy consumption, interoperability issues, data privacy concerns, and limited digital infrastructure among smallholders in regions such as India. This review synthesizes findings across four core application areas—data storage, supply-chain tracking, smart-contract automation, and security/trust—and identifies six open research gaps. It concludes that dApps have strong potential to make agriculture more transparent, equitable, and sustainable, provided that scalability, usability, and regulatory barriers are addressed.
The agricultural sector is essential for global food security but continues to face challenges in supply chain management, including lack of transparency, traceability, and data integrity. This study proposes AgroChain, a blockchain-based framework designed to enhance governance and trust in the Agricultural Supply Chain (ASC). The system is built on the Quorum blockchain platform, an enterprise version of Ethereum, which integrates Zero-Knowledge Proof (ZKP) protocols to ensure data privacy while maintaining secure and transparent transactions. AgroChain introduces a scalable process model that separates the registry of agricultural records from the actual data, enabling efficient data handling. Smart contracts are used to automate key supply chain operations such as record creation, validation, transfer, and deletion, allowing end-to-end traceability from farm to consumer. The framework also incorporates rolebased access control for stakeholders including farmers, distributors, retailers, and consumers. Experimental results indicate that AgroChain improves transparency, accountability, and interoperability, demonstrating the potential of blockchain technology to transform agricultural supply chain governance.
G. Naveen Kumar, B. Vaishnavi, Ch. Gayathri Bharghavi, K. Shveni
The Smart Contract Farming System is a digitalized platform created to connect farmers and buyers more reliably and transparently. Its main goal is to reduce the gap between both buyers and farmers by using secure digitized agreements that clearly define terms and conditions, which helps to build trust and ensures that transactions are fair and well-structured. The platform is developed using Vite and React, through which users can easily register, explore crop listings, view contracts, and interact in real time. On the backend, Node.js and Express.js handle the core application logic, including API services, authentication, contract processing, and transaction management. All data is securely stored and managed using MongoDB, ensuring consistency and reliability. One of the key features of the system is the Price Prediction Module, which works before a contract is finalized. This module uses agricultural datasets collected from IEEE research publications. With the help of Python-based machine learning models, the system predicts crop prices by analyzing historical data, seasonal trends, and market needs and supply. It helps farmers and buyers in making informed decisions and agreeing on fair prices. Once the price is decided, digital contracts are created and accepted by both parties. This system also includes crops based on agricultural seasons such as Kharif, Rabi, and Zaid, which helps in better planning and avoids mismatches between supply and demand. In addition to contract management, the platform involves features like dispute resolution tools and analytical dashboards, which make the overall process more efficient and transparent. Payments are secured through trusted methods such as UPI and escrow systems, ensuring safe and reliable transactions. The feature that makes this application more effective is the Crop Insurance module, which allows farmers to enroll in government-supported insurance schemes. This protects them from unexpected risks like floods, droughts, or pest attacks. Overall, the system is designed to be scalable, efficient, and user-friendly. It strengthens farmers by giving them assured market access while helping buyers get a consistent and trustworthy supply of crops.
International Journal of Technology, Leadership and Sciences
Food and agriculture supply chain transparency is growing in importance for both consumers and states. The fast expansion of blockchain technology's use is being propelled by its inherent trustworthiness and immutability. This technology can offer safe traceability for the management of the agri-food chain, prevent food fraud, and provide information like a food product's provenance. It is far more difficult than in other businesses to create smart contracts that are suitable for certain use cases. Although many agri-food chain management systems based on smart contracts and blockchain have been developed, they are all quite ad hoc and not easily adaptable to different products or production processes. A new method for quickly adapting and developing universal smart contracts for the agri-food business based on Ethereum is presented in this research. We can automate the process and reuse modules and code using this strategy, which shortens development times without sacrificing dependability and safety. In order to set up a semi-automatic system, we want to start with the production process and build the smart contracts that control the system and the user interfaces that automatically connect with them. To further illustrate how our method works, we provide a case research on honey production. The primary goal of future studies will be to find ways to apply the method to different types of supply chains. Even though Ethereum is now in use, our technology can be simply adapted to other blockchain systems.
Munir A. ADEWOYE, Ahmed Aliyu, Usman Ali, Abdulrasheed Jimoh
Food is fundamental to human survival, we eat to live, sustaining ourselves with nutrition that meets our daily needs. Food security, defined as universal physical and financial access to safe and nutritious food, depends heavily on efficient supply chains. However, ensuring this security faces significant challenges in tracking and transparency. This study examines two critical problems in blockchain-based food supply chain tracing: privacy preservation and scalability. While blockchain technology combined with Internet of Things (IoT) devices offers promising solutions for real-time monitoring, transparency, and fraud prevention in agricultural supply chains, questions remain about balancing computational efficiency with privacy protection, achieving scalable integration across multi-actor supply chains without compromising traceability, and implementing these systems in resource limited environments. Through a comprehensive review of current research, this study identifies emerging technologies like Zero Knowledge Proofs (ZKPs) and ZK-Rollups that enhance both throughput and privacy in decentralised systems. The research presents layered architectural models integrating blockchain ledgers, off-chain storage, IoT sensors, and cryptographic protocols to enable secure and scalable traceability. These models support compliance verification while protecting sensitive data and can be adapted for low-resource contexts. The findings demonstrate that scalable, privacy-preserving blockchain technologies can transform agricultural traceability, empowering supply chain stakeholders while maintaining data confidentiality and integrity. The study also identifies future research needs, including cross-chain interoperability, policy integration, cost-benefit analysis for smallholder farmers, and field validation.
Modern precision agriculture depends on safe and effective fertilizer management. However, existing systems lack real-time decision-making capabilities, rarely incorporate secure traceability methods, and mainly concentrate on nutrient prediction without determining the type of soil fertilizer utilized for a specific crop. To classify fertilizer types (organic vs. inorganic) in real-time based on soil nutrient parameters (temperature, pH, EC, N, P, and K), this investigation suggests an innovative, lightweight self-attention transformer neural network (TNN) based Fertilizer class contract network (FCCN) model. The proposed research is one of the first to combine secure blockchain recording, fertigation, and fertilizer-type detection into a single edge-based pipeline that operates in real time. The process integrates blockchain-based transaction logging and IoT-edge computing for recording transparent and secure agricultural activity. Whenever deficits emerge, the suggested method uses Venturi irrigation to automatically activate fertigation after processing real-time sensor data at the edge to determine the types of fertilizer utilized and the nutritional status. This work uses a decentralized and scalable architecture compared to cloud-dependent or AI-based-only models. Fertilizer classification and fertigation actions based on the real-time nutrient level recommendation are recorded as immutable transactions on an Ethereum blockchain using a Proof-of-Stake (PoS) consensus. Before the final on-chain recording, validator logic confirms the accuracy of field data, fertigation events, and real-time soil nutrient levels. Real-time blockchain measurements reveal transaction completion speeds of less than 0.03 seconds, gas consumption of less than 62,000 units, and throughput of 15-35. Experimental findings show that FCCN categorization accuracy surpasses 98.85%.
The Nigerian agricultural financing ecosystem, particularly incentive-based risk-sharing schemes such as the Nigeria Incentive-Based Risk Sharing System for Agricultural Lending (NIRSAL), faces persistent challenges related to data integrity, transparency, and trust among stakeholders. Centralized information systems expose sensitive financial and operational data to risks including unauthorized modification, fraud, lack of auditability, and single points of failure. This study proposes a blockchain-based information security framework designed to enhance transparency, integrity, and accountability in incentive-driven agricultural lending systems. The framework leverages distributed ledger technology, cryptographic hashing, consensus mechanisms, and permissioned access control to ensure tamper-resistant record keeping and secure transaction validation. A conceptual system architecture is developed to demonstrate how lending data, incentives, and risk-sharing records can be securely managed in a decentralized environment. The proposed approach improves trust among financial institutions, regulators, and agricultural stakeholders while reducing fraud, operational inefficiencies, and information asymmetry.
Aqsa Rashid, Raja Wasim Ahmad, Mirna Nachouki, Atta Ur Rehman Khan
Ensuring food safety and traceability in fruit supply chains (FSC) remains a critical concern, as traditional centralized methods often suffer from data manipulation, lack of transparency, and delayed responses during contamination events. These challenges lead to reduced consumer trust and inefficiencies in monitoring product integrity throughout the supply network. To address these limitations, this paper presents a blockchain-based framework that leverages cryptographic protocols and smart contracts to secure, automate, and validate traceability processes across all stages of the fruit supply chain. The proposed FSC_SDG system enforces trusted data recording, real-time provenance verification, and autonomous policy execution, while aligning with the United Nations Sustainable Development Goals (UN-SDGs). A proof-of-concept prototype was implemented on the Ethereum blockchain to assess performance. Experimental evaluations demonstrate reduced latency in traceability verification, improved data integrity, and enhanced resistance to tampering compared with existing approaches. These results confirm the effectiveness of the proposed framework in strengthening food safety, transparency, and trust within fruit supply chains.
R. N. V. Jagan Mohan, Pravallika Sree Rayanoothala, R. Praneetha Sree
Agriculture faces multifaceted challenges including climate variability, soil degradation, and supply chain inefficiencies, particularly for smallholder farmers practicing multicropping. This study systematically integrates blockchain technology for secure, transparent transactions with reinforcement learning (RL)-optimized Neutrosophic multi-regression for precise crop loss prediction in multicropping systems. Using real-world data from six crops (rice, banana, turmeric, elephant foot yam, coconut, cocoa), Neutrosophic multi-regression estimated losses with RL hyperparameter tuning, achieving superior prediction accuracy. A blockchain framework was developed for farmer validation, transaction security, and smart contract execution using Ethereum/Ganache. Results demonstrate 25%–35% reduction in predicted crop losses and enhanced supply chain traceability. This Smart Agriculture 5.0 framework advances Agriculture 4.0 through human-AI symbiosis and uncertainty modeling, addressing single-point failures, data privacy, and trust deficits for scalable sustainable farming Through this multidimensional approach, the study endeavors to not only enhance the productivity and sustainability of agricultural practices but also to foster resilience in the face of evolving challenges.
ABSTRACT The perishable food cold chain is a vital part of the global food system, mainly ensuring the quality of temperature‐sensitive products such as milk, seafood, fruits, and vegetables. However, this system still faces several challenges related to real‐time monitoring, data transparency, and proactive demand planning, which often lead to spoilage, food safety violations, and disruptions. In this work, we propose a comprehensive end‐to‐end system that includes Internet of Things (IoT)‐based environmental sensors, blockchain‐enabled immutable logging, and a hybrid ARIMA–LSTM model for demand forecasting and spoilage risk detection. The system uses ESP32 microcontrollers with DHT11 sensors at key cold‐chain points to collect temperature and humidity data, streamed to Blynk dashboards for real‐time visualization and anomaly alerts. Anomalies and product metadata are permanently recorded through smart contracts on a private Ethereum blockchain, while payloads are stored off‐chain on IPFS for traceability and auditability. Additionally, historical blockchain logs and external sales data are used to train a hybrid ARIMA–LSTM model that predicts future demand and spoilage risks more accurately. Experimental results show that the proposed model achieves a forecasting accuracy of 90% ( R 2 = 0.90), outperforming baseline approaches on multiple metrics including RMSE, MAE, MAPE, and R 2 . The framework is scalable and data‐driven, aiming to improve supply availability and reduce food waste.
A new approach to influence longetivity of crops in a humid and heated area by bitcoin computers aimed to reduce greenhouse emmisions while sustaining food for all.
Rossana Caputo, Bernard Mallia, Keerthi Kumar Masanasetty, Prasad M
Agricultural digitisation is increasingly driven by regulatory compliance, sustainability mandates, and the need for trusted multi-stakeholder collaboration. However, agricultural equipment traceability remains largely manual or dependent on energy-intensive Internet of Things (IoT) infrastructures that are ill-suited for rural environments. This paper presents AgriLink, a privacy-preserving and energy-efficient Distributed Ledger Technology (DLT) framework for agricultural equipment and workforce traceability, developed under the TrustChain OC5 initiative. AgriLink introduces an event-driven architecture based on passive Near Field Communication (NFC), selective blockchain anchoring, zero-trust security, and privacy-by-design principles. By functioning primarily on episodic, event-driven, non-continuous data flows, the framework enables verifiable digital twins of physical agricultural assets without continuous telemetry or battery-powered sensors. This study details the system architecture, threat modelling, privacy mechanisms, and a comparative evaluation against traditional IoT tracking systems. Experimental validations indicate up to a 94% reduction in energy consumption at the sensing layer compared to an average of Bluetooth Low Energy (BLE), ZigBee, and LoRaWAN architectures, while maintaining robust auditability and General Data Protection Regulation (GDPR) compliance. The results establish AgriLink as a viable blueprint for sustainable DLT adoption in physical asset-intensive agricultural sectors.
Béthel Atohoun, A. Charbel Atihou, Mikaël A. Mousse, Mithelle M. J. Alavo
To overcome the structural limitations of traditional hydroponic systems—inefficient input management, lack of verifiable traceability, high energy consumption, and absence of adaptive optimization—this paper presents an innovative architecture that synergistically integrates Distributed Ledger Technology (DLT), Internet of Things (IoT), and Artificial Intelligence (AI) to optimize resource management in controlled hydroponic environments. The proposed architecture constitutes a hybrid DTL, IoT and IA system founded on six principles: radical distribution of trust, defense in depth, verifiable trust through cryptographic proofs, modularity, native interoperability, and scalability. It comprises a distributed intelligent sensor network, a low-cost edge computing cluster, optimized artificial intelligence modules, and a DLT infrastructure based on Hyperledger Fabric with Raft consensus. Experimental results, obtained through system simulation on a 100 m² greenhouse and validated by partial prototyping, demonstrate robust operational performance: average latency of 847 ms from sensor to blockchain, throughput of 150 transactions per second, availability of 99.7%, support for 500 simultaneous sensors, and energy autonomy of 14 months. AI models achieve 96.3% accuracy in nutritional prediction, with pH prediction error of 0.08 units and EC error of 15 µS/cm. DDPG orchestration converges after 45 days with stabilization of the reward function. Comparative analysis reveals significant advantages: 18% yield increase, 15% reduction in input costs, 22% decrease in energy consumption during peak pricing periods, and 40% improvement in total cost of ownership over 5 years.