C.M. Nalayini, A.R. Sathyabama, S. Priyadharshini, Sumathi. S
In the modern agricultural landscape, collaborative robots have emerged as key technology for enhancing precision, efficiency and sustainability in farming practices. This research introduces a Smart Cobot Greenhouse Assistant developed to automate plant health monitoring and management with controlled environment. It identifies plant's condition and take suitable actions like watering dry plants, raising alerts for diseased ones, and leaving healthy plants unharmed. A two-dimensional workspace is modelled where the cobot is initialized at the origin$(\mathbf{0, 0})$and plants are located at respective positions$(\mathbf{x, y})$. Dynamic states such as healthy, dry and diseased are assigned and simulated through python code using a well-defined synthetic dataset. Cobot's movements and actions are animated using Matplotlib visualization to produce an efficient and sustainable greenhouse management and the information are recorded into the blockchain distributed ledger and alerts are sent to the owner for further decision. Compared with the traditional system, the proposed smart cobot achieved operational responsiveness, dynamic visual feedback, and trustworthy data handling with full transparency.
To overcome the structural limitations of traditional hydroponic systemsâinefficient input management, lack of verifiable traceability, high energy consumption, and absence of adaptive optimizationâthis 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 mÂČ 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 ”S/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.
Food security is a serious global issue, concerning the availability, accessibility, safety, and stability of food. The agri-food supply chain, which connects farms to consumers, often faces problems such as fragmented data systems, poor transparency, and low trust between stakeholders. These problems make decision-making slow and reduce the quality and safety of food. During the Fourth Industrial Revolution (IR4.0), digital technologies are transforming many industries, including food sector. Among them, blockchain has emerged as a critical enabler for traceability and accountability. However, balancing transparency and privacy remains a major challenge as sensitive business data must be protected. Without solving this issue, many stakeholders are not ready to accept blockchain solutions. This study analyses current blockchain limitations and proposes a privacy-preserving blockchain solution for agri-food supply chains. Using the Design Science Research Methodology (DSRM), this work identifies key privacy gaps, designs a solution integrating selective data sharing, access control, and privacy-preserving techniques such as zero-knowledge proofs and differential privacy and outlines future empirical validation through prototype implementation. These features aim to balance open traceability with the need to keep important information private. The results suggest that a privacy-preserving blockchain can enhance trust, protect private data, and maintain transparency in the food chain. This makes the system more resilient and reliable. At the same time, it supports the United Nations goals, especially Goal 2 (Zero Hunger) and Goal 12 (Responsible Consumption and Production), by helping to build food supply chains that are safe, fair, and sustainable.
Ibsen G. Bazie, Alidor M. Mbayandjambe, Kevin nguemdjom, Alain M. Kuyunsa · 9 authors
Africaâs agricultural sector employs over 60% of the continentâs population but faces challenges in product traceability and food security due to limited infrastructure and information asymmetries. This paper presents a blockchain-based traceability framework specifically designed for African agricultural supply chains, integrating smart contracts with electronic labelling (E-labelling) technologies to address socioeconomic constraints. We developed a decentralized system using Ethereum blockchain platform, implemented through Solidity smart contracts and a NextJS web application, optimized for low-bandwidth environments common in sub-Saharan Africa. The framework incorporates automated QR code generation enabling smallholder farmers to participate in transparent supply chains without extensive technical expertise. A comprehensive analysis of 13 blockchain applications revealed gaps in addressing African-specific challenges such as limited connectivity, multilingual requirements, and diverse regulatory environments. The proposed system is evaluated using the ADJENDE agribusiness case study design parameters from Burkina Faso, demonstrating practical applicability in West African contexts. Experimental validation shows functional product traceability with average transaction processing times of 20-25 seconds and gas costs of approximately 6,000,000 units per transaction. The prototype demonstrates capability for farmers to register crop information, track processing stages, and provide consumers with verifiable product authenticity through QR code scanning. This work presents a blockchain framework prototype designed for African agricultural contexts, addressing critical challenges of food security, rural economic empowerment, and consumer protection across the continent.
ABSTRACT To address the core challenges of information asymmetry, privacy leakage, and low storage efficiency in rice supply chains, this study proposes an enhanced traceability system that integrates blockchain, adaptive encryption, and lightweight zeroâknowledge proofs. The system features a dynamic roleâbased encryption model, where encryption levels are determined by both data sensitivity and roleâbased weights. This model was designed and validated through surveys involving 50 stakeholders. By adopting an onâchain and offâchain collaborative storage architecture that leverages Merkle trees and IPFS, the system achieves a 67% reduction in storage overhead. Furthermore, an optimized Groth16âbased ZKP protocol ensures rapid verification in under 180 ms on ARMâbased devices. Experimental results demonstrate that, at a scale of 100,000 records, the system attains a transaction processing capacity of 328 TPS and an information entropy of 3.87, representing a 51% improvement over singleâlayer encryption schemes. The monthly deployment cost remains affordable for smallholder farmers, ranging from $2 to $5. The system also supports interoperability with external traceability frameworks through crossâchain channels and adaptation to the GS1 EPCIS standard, facilitating trusted collaboration in transnational rice supply chains. By effectively balancing data integrity and privacy protection, this solution significantly enhances system scalability and offers a novel pathway for the digital transformation of agricultural supply chains.
The scarce resource in agriculture needs to be managed efficiently, and we are developing new solutions to meet our need to manage resource scarcity and to improve irrigation methods. This research proposes the blockchain-enabled Decentralized Water Management System (BD-WMS) based on Blockchain, Smart Contract, Internet of Things (IoT), and Artificial Intelligence (AI) for sustainable irrigation. On a real-time basis, and to see that the data collected is accurate, the BD-WMS is loaded with IoT sensors to measure the soil moisture, pH levels, and weather conditions. Firstly, it records the data in a ledger on blockchain to ensure that there is no corruption of data and that the data cannot be changed in any way. Using smart contracts, dynamic water requirements are complied with to autonomously control irrigation valves according to dynamic water requirements. I also put forth a Tokenized Water Conservation Incentive Model (TWCIM) that distributes blockchain-based tokens to the farmers in exchange for their adoption of water-saving practices that are convertible into a subsidy amount or can be spent on agricultural resources. An AI-powered predictive analytics module plays its part in the further development of the system efficiency, and it predicts the water demands based on the historical data and environmental conditions. In the greenhouse tomato, the studies show up to 40% water savings and about 25% increase in crop yield when compared to conventional water management. It offers a unique solution to the problems that occur in the traditional irrigation model owing to the decentralized control, along with the criteria of incentive-driven conservation. It was proposed as a scalable, secure, and efficient solution to support sustainable agriculture that optimizes efficient water governance and resource preservation.
Shahid Salim, J. V., Giovanni De Gasperis, Diego Valdeolmillos · 6 authors
Smart agriculture is transforming a traditionally static sector by introducing advanced monitoring of crop processes and field conditions. In particular, the integration of the Internet of Things with Distributed Ledger Technology enhances agricultural operations by enabling real-time insights and fostering trust through secure, tamper-proof data management. This paper presents an innovative system that leverages IOTAâs decentralized ledger to securely capture and store realtime data from IoT sensors monitoring key environmental parameters such as temperature, humidity, and soil moisture. By removing centralized control, the system ensures data integrity, transparency, and resistance to tampering. Additionally, the use of smart contracts developed in the Move programming language strengthens the platform by automating data validation and facilitating traceable, reliable interactions. Field implementation demonstrates the systemâs potential to improve decision-making, minimize resource waste, and support sustainable agricultural practices. Emphasizing security, scalability, and cost-efficiency, this solution offers a forward-looking approach to precision agriculture.
Modern agriculture faces critical challenges such as climate change, food security and supply chain inefficiencies, which demand innovative solutions. Traditional farming systems often lack real time monitoring, data security and transparency, leading to wastefulness and quality concerns. To address these, we present a comprehensive precision agriculture framework that integrates Internet of Things (IoT) sensors, Raspberry Pi (R-Pi) edge computing, blockchain based data management and computer vision (CV) assisted statistical modeling. The system collects environmental data via a sensor network, processes it at the edge using R-Pi, and records summarized outputs on a secure Ethereum based blockchain using smart contracts. Simultaneously, CV modules perform real time quality assessment and anomaly detection. A Markov chain based stochastic model is employed to track quality degradation in high value crops. The methodology is validated through a saffron use case, demonstrating effectiveness in monitoring filament degradation and detecting potential fraud. This integration enhances real time decision making, ensures traceability and promotes sustainability in climate smart agriculture.
There has been a growing need for secure, transparent, and efficient systems to manage agricultural processes. Traditional methods often lack traceability, leading to inefficiencies, fraud, and data manipulation. Additionally, centralized agricultural management systems are prone to security risks and single points of failure. Blockchain technology provides a decentralized and secure solution that enhances transparency, security, and efficiency in agriculture. By utilizing a distributed ledger, blockchain ensures end-to-end traceability of agricultural products, preventing fraud and ensuring quality control. Farmers, suppliers, and consumers can verify the authenticity of products without relying on intermediaries, thereby reducing costs and enhancing trust in the supply chain using the proposed scheme.
This paper presents a novel blockchain-based incentive system that protects farmers' privacy while promoting sustainable farming methods by using zero-knowledge proofs, or ZKPs. With this method, farmers can show that they've met their yield goals without giving away private production information. We implement the system as an Ethereum smart contract that uses cryptographic assurances to ensure correct reporting and distributes incentives based on verified crop yields. Our method addresses important problems in agricultural sustainability projects, like protecting privacy, the cost of verification, and making sure that rewards are shared fairly. The suggested answer affects sustainable agriculture policy, privacy-focused data exchange in supply lines, and the use of cryptographic methods in environmental governance.
Blockchain Technology Applications and Security
Smart Agriculture and AI
Physical Unclonable Functions (PUFs) and Hardware Security
This article studies the benefits, challenges and future potential of combining blockchain, Distributed Ledger Technologies and Artificial Intelligence in digital agriculture. Their convergence presents significant opportunities in critical areas of agriculture, such as supply chain traceability, financial inclusion, environmental monitoring, and decentralized data governance. These technologies can shape the future of digital agriculture by developing inclusive, intelligent, and decentralized systems that are both technologically advanced and aligned with social equity and environmental sustainability.
This paper proposes MetaCropX, a smart agriculture framework that introduces a dual-token design to represent and track field-level events as they occur. Upon detecting a real-time crop event, a fungible AgriTrack Token (ATT) is minted and incrementally updated using stack-based metadata appends, maintaining an immutable record of disease status, treatments, and environmental conditions. Role-Based Access Control (RBAC) enforced via smart contracts ensures that only authorized stakeholders contribute metadata. At the end of the crop cycle, selected ATTs are converted into a single non-fungible AgriProof Asset (APA) token consisting of the verified crop lifecycle. On the other hand, off-chain InterPlanetary File System (IPFS) storage reduces on-chain data load and supports efficient metadata management. Simulated crop scenarios validate transitions, role enforcement, and metadata integrity. The proposed framework enhances transparency and traceability by allowing the stakeholders to verify the crop data in a secure manner.
Ravi Kumar Munaganuri, Yamarthi Narasimha Rao, Sai Chandana Bolem
This research is anchored on the burning need for irrigation optimization and crop water use efficiency improvement, which remains a challenge in smart agriculture processes. Traditional irrigation methods normally lead to inefficiency, resulting in wasted water and non-maximum crops. These traditional ways normally lack attributes of real-time adaptability and secure data managementâthings that are very key to modernizing agricultural practices. In this work, artificial intelligence (AI), Internet of Things (IoT), and blockchain techniques will be integrated to design a comprehensive system for monitoring and predicting soil moisture levels. In the proposed model, long short-term memory (LSTM) networks are considered for soil moisture level prediction, taking into consideration past data, weather, and crop type. LSTM networks are chosen here for their high performance in timestamp series prediction tasks with an mean average error (MAE) of 0.02 m 3 /m 3 over a 7-day forecast horizon. For real-time monitoring, IoT sensors based on long range wide area network (LoRaWAN) technology are field-deployed for conducting long-range communications while consuming very limited energy to extend the sensor battery life over 5 years and bring down the data transmission latency below 5 s. It has an inbuilt permissioned blockchain frameworkâHyperledger Fabricâwhich offers a secure and transparent system for data management and maintaining a record of soil moisture data, irrigation events, and metadata from sensors. This ensures the immutability and integrity of sets of data. Smart contracts automate irrigation upon reaching preconfigured soil moisture thresholds, and hence zero data integrity breaches occur with a transaction throughput of 1,000 transactions per second, taken into view with smart contract execution latency of less than 2 s. Moreover, it utilizes reinforcement learning with Deep Q-Learning to derive an optimized irrigation schedule. In this regard, it enables learning optimal irrigation policies and implements them to improve efficiency in the usage of water by 25% and increases crop yield by 15% compared to the traditional methods. Clearly from field trials, results indicate evident efficiency of the integrated system: a 20% water usage reduction and a 12% increase in crop yield within one growing season. This is rather an innovative take on irrigation practices, increasing a great deal of accuracy and sustainability for such and providing a really strong solution toward better agricultural productivity and resource management.
Inzamam Shahzad, Muhammad Wajid Maqsood, Sadia Latif, Hafiz Muhammad Ijaz
As communication technologies evolve, the IoT has transitioned from nascent development to near maturity, driving exponential growth in data transmission and processing. This advancement imposes increasingly stringent performance requirements on the management of globally distributed IoT infrastructures. Current centralized IoT device management platforms, however, face critical technical limitations, including vulnerability to cyber-attacks, single points of failure, and scalability challenges. To address these issues while adhering to regulatory mandates for data confidentiality, this study proposes a blockchain-integrated IoT sensor system designed to enhance data security, transparency, and accessibility. The framework combines IoT-based sensor networks with blockchain technology to establish an immutable, decentralized ledger for device interactions, ensuring tamper-resistant data records and secure access control. A smart contract governs the applicationâs business logic, automating rules for user-device interactions, data monitoring, and device management. The systemâs efficacy is validated through a prototype implementation using NodeMCU microcontrollers and permissioned blockchain networks, with performance evaluated across metrics such as latency, throughput, and resource utilization. A case study in cotton field agriculture demonstrates the platformâs practical application, integrating irrigation automation to optimize water consumption. Empirical results indicate a 35% reduction in water usage while maintaining crop yield, alongside robust resistance to unauthorized data tampering. Comparative analysis highlights the solutionâs superiority over centralized alternatives in scalability and resilience, particularly for resource-constrained IoT environments. By harmonizing IoTâs sensing capabilities with blockchainâs decentralized security, this work advances agricultural management practices, offering a robust, transparent, and efficient paradigm for modern IoT deployments. The findings underscore the transformative potential of blockchain-IoT integration in fostering sustainable, data-driven decision-making across diverse industrial sectors.
Mahesh Prasanna K, S. Chandrappa, K. B. V. Brahma Rao, H K Bhargav · 6 authors
The reliability of decision-making depends on ensuring data integrity when data comes from sensor networks in smart agriculture systems. The research develops a blockchain-supported secure communication model which protects against weaknesses in agricultural IoT systems. Distributed ledger architecture along with smart contract validation protocols forms the basis of the model for authenticating sensor data. Testing conducted in several agricultural settings confirmed data verification reached 99.7% accuracy and the detection of tampering achieved a success rate of 98.2% while authentication methods operated with 43% faster speed than conventional techniques. All simulated security breach attempts failed to penetrate the system which operated effectively in different field conditions. A scalable solution now provides agricultural data protection capabilities which allow farmers and agribusinesses to trust their sensor data for advanced crop management efficiency and resource planning and yield assessment.
Distributed Ledger Technologies (DLT), such as Bitcoin, Ethereum, and Directed Acyclic Graphs (DAG), are being positioned as a promising solution for smart agriculture by enabling secure, decentralized, and transparent traceability systems. However, these technologies face challenges related to scalability, latency, and efficiency in IoT environments. In this study, we conduct a comparative analysis of Bitcoin, Ethereum, and DAG technologies through extensive simulations, varying transaction generation rates and network latencies. A key methodological innovation of this research is the detailed codification of agricultural data transactions, encompassing parameters such as crop type, fertilization, harvesting, and transportation, enabling a structured and scalable approach to data representation. Our results reveal that Bitcoin's robustness is hindered by its high sensitivity to latency and network load, with inclusion times exceeding 700 s. Ethereum demonstrates better adaptability, with controlled inclusion times ranging from 12.91 to 35.76 s under varying conditions. DAG outperforms both, achieving significantly lower inclusion times between 4.27 and 22.25 s, highlighting its suitability for real-time applications. To the best of our knowledge, this is the first study to provide a direct comparison of these technologies in the context of agricultural traceability, showcasing the advantages and limitations of DAG-based systems for managing and scaling agricultural IoT networks.
Najmus Sakib Sizan, Md. Abu Layek, Khondokar Fida Hasan
To improve crop forecasting and provide farmers with actionable data-driven insights, we propose a novel approach integrating IoT, machine learning, and blockchain technologies. Using IoT, real-time data from sensor networks continuously monitor environmental conditions and soil nutrient levels, significantly improving our understanding of crop growth dynamics. Our study demonstrates the exceptional accuracy of the Random Forest model, achieving a 99.45\% accuracy rate in predicting optimal crop types and yields, thereby offering precise crop projections and customized recommendations. To ensure the security and integrity of the sensor data used for these forecasts, we integrate the Ethereum blockchain, which provides a robust and secure platform. This ensures that the forecasted data remain tamper-proof and reliable. Stakeholders can access real-time and historical crop projections through an intuitive online interface, enhancing transparency and facilitating informed decision-making. By presenting multiple predicted crop scenarios, our system enables farmers to optimize production strategies effectively. This integrated approach promises significant advances in precision agriculture, making crop forecasting more accurate, secure, and user-friendly.
This study integrates blockchain technology into smart agriculture to enhance its productivity and sustainability. By combining blockchain with remote sensing, artificial intelligence (AI), and the Internet of Things (IoT), a Human-Cyber-Physical System (H-CPS) architecture tailored for agricultural applications is proposed. It supports real-time crop management, data-driven decision-making, and transparent trading of agricultural products. A semantic-based blockchain framework is introduced to address challenges in data management and AI model integration, optimizing production, improving traceability, reducing costs, and enhancing financial security. This framework directly addresses real-world agricultural challenges, such as optimized irrigation, improved crop breeding efficiency, and enhanced supply chain transparency. These innovations provide practical solutions for modern agriculture, contributing to sustainable development and global food security. Further research and collaboration are encouraged to unlock its full potential in transforming agricultural practices.
In recent years, the convergence between blockchain and artificial intelligence (AI) has led to significant innovations in the agricultural sector, particularly in the traceability and protection of grains. These emerging technologies have the potential to transform the agricultural supply chain, providing greater transparency, security, and efficiency. Blockchain technology, with its ability to create immutable and transparent records, is widely applied to trace the origin and movement of grains from production to the final consumer. At the same time, AI plays a key role in analyzing large volumes of data, allowing for the prediction of risks and the dynamic adaptation of agricultural insurance contracts. Additionally, the combination of blockchain and AI facilitates the creation of new financing models, such as smart contracts, which automatically execute when certain conditions are met. These advancements help ensure the quality of grains, combat fraud, optimize logistics processes, and respond more swiftly to unforeseen events. The integration of these technologies also contributes to more sustainable, efficient, and resilient agriculture, addressing challenges such as climate change, price volatility, and the increasing demand for transparency in the supply chain. The combined use of blockchain and AI is reshaping grain production and traceability, providing a safer and more efficient system for the future of agriculture, particularly in the United States.
In smart-agriculture, AI is held back by fragmented, hard-to-access datasets. To address this problem, we propose a Decentralized Autonomous Organization (DAO) that couples a consortium Product-Chain (P-Chain) for data custody with a Value-Chain (V-Chain) for decentralized trading. P-Chain registers raw, processed data and model artefacts; V-Chain runs transparent auctions where researchers, farmers, label-service providers and start-ups exchange data, labels or trained models as reusable digital products. Smart-contract pricing and random-validator consensus guarantee authenticity, privacy and fair value flow while preventing collusion. A weed-eradication case study shows that buying ready-made data, labels and models on the platform cuts development cost by 76% compared with in-house collection and training. The architecture thus lowers entry barriers, accelerates AI model iteration, and paves the way for sustainable, data-driven precision agriculture.
Sajid Safeer, Giuseppe De Mastro, Cataldo Pulvento
Modern-day agriculture is vital for sustainable production, ensuring a consistent supply of food and fiber for humanity. The data proving its quality is economically significant, encompassing farm conditions, irrigation practices, inventories, contracts, and deals within the agro-food supply sector. To ensure transparent and secure data transfer and storage, a trustworthy interconnected databank is essential for all concerned authorities and contributors. The integration of Internet of Things (IoT) in agriculture with blockchain technology offers an unparalleled solution. This combination serves as a distributed ledger, ensuring transparent and secure management of critical environmental and supply chain data. The IoT-based blockchain infrastructure enhances agricultural sustainability and environmental monitoring. It is anticipated that this technology will become increasingly accurate and effective in addressing persistent challenges in the agro-food sector. This bibliometric analysis reviews and synthesizes relevant literature from the Scopus database, highlighting the growth and trends in IoT and blockchain research applied to precision agriculture. The study reveals a remarkable 47.58% annual growth rate in research within this field, starting with only three published documents in 2019 and peaking at 21 in 2022 and 20 in 2024. Globally, China and India lead in publication output, collectively accounting for 62% of the articles. In terms of citations, India ranks highest with 550 total citations, followed by Italy with 431 citations during 2019â2024. This comprehensive study serves as a valuable reference for understanding the research trends and growth in IoT and blockchain applications in agriculture, providing critical insights for future developments in this rapidly evolving field.
Redmond R. Shamshiri, Abdullah Kaviani Rad, Maryam Behjati, Siva K. Balasundram
The challenges and drawbacks of manual weeding and herbicide usage, such as inefficiency, high costs, time-consuming tasks, and environmental pollution, have led to a shift in the agricultural industry toward digital agriculture. The utilization of advanced robotic technologies in the process of weeding serves as prominent and symbolic proof of innovations under the umbrella of digital agriculture. Typically, robotic weeding consists of three primary phases: sensing, thinking, and acting. Among these stages, sensing has considerable significance, which has resulted in the development of sophisticated sensing technology. The present study specifically examines a variety of image-based sensing systems, such as RGB, NIR, spectral, and thermal cameras. Furthermore, it discusses non-imaging systems, including lasers, seed mapping, LIDAR, ToF, and ultrasonic systems. Regarding the benefits, we can highlight the reduced expenses and zero water and soil pollution. As for the obstacles, we can point out the significant initial investment, limited precision, unfavorable environmental circumstances, as well as the scarcity of professionals and subject knowledge. This study intends to address the advantages and challenges associated with each of these sensing technologies. Moreover, the technical remarks and solutions explored in this investigation provide a straightforward framework for future studies by both scholars and administrators in the context of robotic weeding.