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 2019–2024. 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.
The production of digital signatures with blockchain constitutes a prerequisite for the security of electronic agriculture applications (EAA), such as the Internet of Things (IoT). To prevent irresponsibility within the blockchain, attackers regularly attempt to manipulate or intercept data stored or sent via EAA-IoT. Additionally, cybersecurity has not received much attention recently because IoT applications are still relatively new. As a result, the protection of EAAs against security threats remains insufficient. Moreover, the security protocols used in contemporary research are still insufficient to thwart a wide range of threats. For these security issues, first, this study proposes a security system to combine consortium blockchain blocks with Edwards25519 (Ed25519) signatures to stop block data tampering in the IoT. Second, the proposed study leverages an artificial bee colonizer (ABC) approach to preserve the unpredictable nature of Ed25519 signatures while identifying the optimal solution and optimizing various complex challenges. Advanced deep learning (ADL) technology is used as a model to track and evaluate objects in the optimizer system. We tested our system in terms of security measures and performance overhead. Tests conducted on the proposed system have shown that it can prevent the most destructive applications, such as obfuscation, selfish mining, block blocking, block ignoring, blind blocking, and heuristic attacks, and that our system fends off these attacks through the use of the test of the Scyther tool. Additionally, the system measures performance parameters, including a scalability of 99.56%, an entropy of 60.99 Mbps, and a network throughput rate of 200,000.0 m/s, which reflects the acceptability of the proposed system over existing security systems.
Sri Sai Durga Mani Vasireddy, Supriya Yalagala, Jayasri Sikha, Rani Vullaganti · 7 authors
The primary objective of this project is to develop a robust and secure system that uses blockchain technology as well as Ethereum and IoT sensors to create a transparent and automated irrigation management solution for farmers. The key components of this innovative system include IoT sensors for real-time data collection, smart contracts on the Ethereum blockchain for transparent and immutable record keeping, and a user-friendly interface for farmers to monitor and control their irrigation systems remotely. The blockchain will provide data security to avoid the problem of data manipulation; because the IoT components will produce large volumes of data, the data must be securely maintained.
Development of Agriculture 4.0 brings higher demands for managing agricultural products. Blockchain technology can enhance the transparency, traceability, and security of the supply chain. Traditional single-blockchain model faces the scalability limitations. Therefore, we propose multilayer blockchain system for agricultural data management to enable the precise management in various aspects, such as supply chain tracing, land management, market transactions, and sustainability tracking. Interplanetary file system helps to alleviate the burden on data storage. Based on the characteristics of system data flow, we devise a post-quantum cross-blockchain data exchange approach using attribute-based encryption (ABE-PQCBCDEA). It solves the scalability problem and realizes fine-grained access control of different blockchain data, and it significantly reduces the size of ciphertext sets for ciphertext reuse. ABE-PQCBCDEA obtains the 42.6% improvement in the encryption and decryption efficiency compared with previous approaches, and its communication costs have been reduced by 48.5%, thereby it reduces the burden of cross-chain data exchange.
Abstract Agritech uses advanced technologies to boost the efficiency, sustainability, and productivity of farming. The Internet of Things (IoT) in agriculture has brought sensors and networked technology to gather and analyse environmental and crop data, enabling precision farming that optimises resource usage and increases yields. Yet, current agricultural methods suffer from unsecured and decentralised data management, causing inefficiencies and complicating traceability across the supply chain. The integration of IoT with blockchain technology is seen as a promising solution to enhance data‐driven agriculture. Blockchain provides a secure, decentralised, and transparent ledger that enhances data integrity, reduces fraud, and improves traceability, which complements IoT applications. The authors detail the development of an innovative system that orchestrates IoT and blockchain technologies to facilitate the adoption of new technologies in agriculture and overcomes the lacked of comprehensive data connectivity. It outlines a conceptual framework and its preliminary empirical implementation. The system consists of three integrated layers: the IoT layer, which creates digital twins of field crops; the blockchain layer, which secures and manages data from the field and external stakeholders for dynamic applications such as track and tracing; and the orchestration layer, which fuses physical and digital data to optimise business models, enhance supply chain productivity, and support governmental policy‐making, thereby improving field productivity and food sector innovation.
Remo Pareschi, Valentina Piantadosi, Sandro Pullo, Francesco Salzano
The agri-food sector stands at a critical juncture, facing the dual challenges of meeting the growing global food demand and ensuring environmental sustainability. This paper explores the transformative potential of integrating Internet of Things (IoT) sensors, Distributed Ledger Technology (DLT), and Machine Learning (ML) to address these challenges. IoT sensors collect real-time data on agricultural conditions, enabling precision farming and efficient resource management. DLT, including blockchain and IOTA’s Tangle, offers a secure and transparent framework for managing this data, ensuring integrity and facilitating trust among stakeholders. ML algorithms analyze the data to predict trends, optimize farming practices, and enhance decision-making. This paper highlights the significant reductions in resource consumption and environmental impact achieved by this integrated approach. The synergy between IoT, DLT, and ML not only enhances agricultural productivity and sustainability but also aligns with the Sustainable Development Goals (SDGs), offering a comprehensive solution to the sector’s pressing challenges. However, widespread adoption faces technical, economic, and social hurdles. Addressing these challenges through continued innovation and collaboration is crucial for realizing the full potential of these technologies in creating a sustainable, efficient, and food-secure future.
Wahidur Rahman, Muhammad Minoar Hossain, Md. Mahedi Hasan, Md. Sadiq Iqbal · 7 authors
Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a realtime framework that incorporates the Internet of Things (IoT), Blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the Internet of Things (IoT) and Blockchain technologies facilitates establishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the Blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 seconds after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms.
Jing Nie, Haochen Li, Yang Li, Jingbin Li · 6 authors
The healthy development of cotton industry is of great significance to the economy of Xinjiang, and the effective management of pests and diseases is the key to ensure the stable development of cotton industry. How to improve the efficiency of cotton pest and disease model detection and get better training effect is a key issue in the task of cotton pest and disease management. Based on the incremental detection model, this article combines the UAV and blockchain sharding technology to create a new cotton pest and disease detection framework, UAV-IFOD-shard. First, the backbone network of YOLOv5n is replaced with ShuffleNetV2, and the squeeze and excitation module is introduced to maintain accuracy and speed. Optimize the neck network using deeply separable convolution to reduce parameters and computation. Improve path aggregation network fusion by replacing concatenation with additive fusion to reduce the number of parameters. Then, an incremental learning method based on knowledge distillation for cotton pest and disease targets is proposed on the basis of the lightweight model to realize parameter updating and memory retention for new and old targets. In addition, the blockchain is further partitioned and a reputation evaluation mechanism is added to the process of federated learning model aggregation to optimize the whole federated learning process. Finally, pest and disease images were collected from cotton fields in several surrounding areas by UAV to construct a dataset on which distributed federation learning was trained. The experimental results show that our model achieves better results than some existing methods, with a reduction of about 69.95% in model parameters, 60% in training time, and only a loss of 5.7% in accuracy. The UAV-IFOD- shard framework improves the system throughput of federated learning and the quality of the aggregated model, and also shows better performance in the face of malicious node attacks, and it is a good choice to use this framework for cotton pest and disease detection in Xinjiang.
The main aim of the proposed system is to facilitate secure and protected land registry in the domain of agriculture Industry 5.0. Considering the outlook of issues associated with it, we considered the blockchain and AI-based technology to fulfill the purpose of secure land registry. Establishing and confirming land ownership is essential for the land registry system in ensuring the protection of ownership rights, particularly crucial in the contexts of agriculture and Industry 5.0. In these sectors, land serves as a crucial resource for sustainable development and industrial innovation. Most of the existing works rely on legacy and centralized system to store land records; which result in high incidences of forgery and fraud. Therefore, maintaining a robust land registry system is essential to fostering economic investments, promoting green practices, and facilitating equitable access to land resources in agriculture and Industry 5.0 ecosystem. We proposed an AI and blockchain-enabled land registry system for agriculture and industry 5.0 that offers a more reliable, transparent, and efficient solution to the challenges of lack of transparency, data tampering, and inefficiency, which can result in disputes, fraudulent claims, and a lack of trust during the land registry. AI models, such as logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM), are employed to classify the fraud and non-fraud land data. Only the non-fraud land data is forwarded into the blockchain network, thereby reducing the computational overhead of the proposed land registry system. In the blockchain network, we designed various smart contracts that validate the land data with unparalleled efficiency and security. Further, the slither solidity source analyzer tool is used for smart contract vulnerability assessment. After the assessment, the smart contract is deployed using the Sepolia test network. The non-fraudulent land data is redirected to the interplanetary file system (IPFS) that stores the original data and forwards the associated hash into the blockchain’s immutable ledger. The entire proposed system is evaluated with different performance parameters, such as AI statistical measures including accuracy, ROC, log-loss score, blockchain scalability comparison, gas cost utilization, and bandwidth utilization. Furthermore, the vulnerability assessment of the smart contract is analyzed using Slither to highlight the working of proposed system without any vulnerabilities. The proposed blockchain and AI-based land registry system ensure a secure and intelligent pipeline to combat against land forgery activities.
M R Shrihari, J Lubna Saira, N Ajay, M. Mahesh · 6 authors
The advancement of smart farming, a crucial aspect of the Internet of Things (IoT), facilitates data-driven insights to enhance agricultural efficiency. However, the widespread deployment of IoT devices presents notable concerns related to data security and integrity. This paper introduces AgriChainSynch, a robust framework integrating blockchain, IoT, and artificial intelligence (AI) to strengthen the security, privacy, and operational efficiency of smart farming ecosystems. The framework utilizes a distributed ledger system to ensure tamper-proof data management, incorporates a Blockchain Integration Layer (BIL) for scalability, and features a Feedback and Adaptation Module (FAM) for continuous performance enhancement. By leveraging AWS Cloud, ESP32, and Ethereum Rinke by smart contracts, the system is capable of detecting and mitigating security threats in real time. Experimental evaluations demonstrate improvements in network efficiency, data storage optimization, and transaction processing speed. Additionally, the study establishes a link between faster threat response times and increased blockchain transaction success rates. The results underscore the feasibility of integrating blockchain, AI, and IoT to develop secure, scalable, and efficient precision agriculture solutions.
The study introduces an efficient data aggregation technique for smart agriculture by leveraging Blockchain technology and a novel method referred to as the "cluster head sleep schedule." The primary objective is to enhance the data collection process within a large-scale agricultural setting where multiple sensors continually generate vast amounts of data while monitoring and safeguarding crops from pest attacks. The proposed method involves the segmentation of sensors into clusters, each led by a designated cluster head responsible for collecting data from its constituent members deployed in the field to monitor pest attacks and promptly report any issues to the management. To curtail data redundancy, the study employs a fuzzy matrix to group nodes based on high-similarity data. This approach enables the selective suspension of certain nodes while others remain active. The data received from these nodes undergoes analysis using a fuzzy similarity matrix for clustering, ensuring that only unique data is transmitted to the base station. Redundant nodes from all clusters are identified and placed in a sleep mode, thus conserving energy and prolonging the network’s lifespan. This sleep scheduling mechanism is implemented subsequent to data redundancy reduction, facilitating immediate pest attack control in agriculture. By implementing these techniques, smart agriculture stands to benefit from optimized energy utilization and reduced costs associated with monitoring and pest control, thereby fostering sustainable and efficient operations. The cluster head is responsible for storing the data on a base station positioned at the network’s edge, allowing for local processing and prompt communication of pest attack information to the farmer for immediate action. Moreover, this edge system stores the data on a Blockchain network for future analysis and serves as a guideline for pest attack control in the pesticide industry, thereby enhancing data security and immutability. In addition to these advantages, the research also emphasizes the importance of controlling pest attacks to enhance crop production in the field, ultimately contributing to the country’s economic growth. Simulation results affirm that the proposed approach leads to notable cost reductions, decreased energy consumption, improved crop production, precise crop monitoring to prevent pest attacks, and a prolonged network lifespan. These outcomes underscore the effectiveness of this approach within the context of smart agriculture and its role in enhancing the monitoring system for smart agriculture and bolstering security through Blockchain technology.
Okacha Amraouy, Yassine Boukhali, Aziz Bouazi, Mohammed Nabil Kabbaj · 5 authors
In recent years, IoT has been increasingly applied in agriculture to transform traditional farming practices into smart and precision agriculture (PA) that are more efficient, productive, and sustainable. However, its implementation in agriculture faces several challenges, including network coverage, reliability, lack of flexibility, and scalability. To address these challenges, current research has focused on developing new communication protocols and technologies, along with several IoT architectural design patterns, especially those based on SOA, which play a crucial role in designing service-oriented solutions. This chapter presents comprehensive and impactful solutions for blockchain-based IoT applications in PA. It proposes novel models combining IoT, blockchain, fog and cloud computing for the development of decentralized applications with independent, autonomous, and interoperable functionalities and services based on the SOA approach. Also, technical challenges, research directions, and the recent advances towards an optimized blockchain-based IoT ecosystem for PA are presented.
Addressing the critical challenges of resource inefficiency and environmental impact in the agrifood sector, this study explores the integration of Internet of Things (IoT) technologies with IOTA’s Tangle, a Distributed Ledger Technology (DLT). This integration aims to enhance sustainable agricultural practices, using rice cultivation as a case study of high relevance and reapplicability given its importance in the food chain and the high irrigation requirement of its cultivation. The approach employs sensor-based intelligent irrigation systems to optimize water efficiency. These systems enable real-time monitoring of agricultural parameters through IoT sensors. Data management is facilitated by IOTA’s Tangle, providing secure and efficient data handling, and integrated with MongoDB, a Database Management System (DBMS), for effective data storage and retrieval. The collaboration between IoT and IOTA led to significant reductions in resource consumption. Implementing sustainable agricultural practices resulted in a 50% reduction in water usage, 25% decrease in nitrogen consumption, and a 50% to 70% reduction in methane emissions. Additionally, the system contributed to lower electricity consumption for irrigation pumps and generated comprehensive historical water depth records, aiding future resource management decisions. This study concludes that the integration of IoT with IOTA’s Tangle presents a highly promising solution for advancing sustainable agriculture. This approach significantly contributes to environmental conservation and food security. Furthermore, it establishes that DLTs like IOTA are not only viable but also effective for real-time monitoring and implementation of sustainable agricultural practices.
Crop tracking and traceability are crucial aspects of the modern agriculture supply chain, ensuring the safety and authenticity of products for consumers while improving efficiency and reducing waste. This paper presents a secure and efficient solution for crop tracking in agriculture by leveraging blockchain technology. The proposed system employs distributed ledger technology to record and verify the journey of crops from the field to the consumer, enhancing transparency and accountability. We explore the integration of smart contracts to automate key supply chain processes, such as quality assessment and payment settlement. Our solution not only strengthens the security of crop data but also streamlines the supply chain, reducing administrative overhead. We demonstrate the feasibility of our approach through a practical implementation, highlighting the benefits of blockchain in agriculture supply chain management.
Despite its status as one of the most ancient sectors worldwide, agriculture continues to be a fundamental cornerstone of the global economy. Nevertheless, it faces obstacles such as a lack of trust, difficulties in tracking, and inefficiencies in managing the supply chain. This article examines the potential of blockchain technology (BCT) to alter the agricultural industry by providing a decentralized, transparent, and unchangeable solution to meet the difficulties it faces. The initial discussion provides an overview of the challenges encountered by the agricultural industry, followed by a thorough analysis of BCT, highlighting its potential advantages. Following that, the article explores other agricultural uses for blockchain technology, such as managing supply chains, verifying products, and processing payments. In addition, this paper examines the constraints and challenges related to the use of blockchain technology in agriculture, including issues such as scalability, legal frameworks, and interoperability. This paper highlights the potential of BCT to transform the agricultural industry by offering a transparent and secure platform for managing the supply chain. Nevertheless, it emphasizes the need for involving stakeholders, having clear legislation, and possessing technical skills in order to achieve effective implementation. This work utilizes a systematic literature review using the PRISMA technique and applies meta-analysis as the research methodology, enabling a thorough investigation of the present information available. The results emphasize the significant and positive effect of BCT on agriculture, emphasizing the need for cooperative endeavors among governments, industry pioneers, and technology specialists to encourage its extensive implementation and contribute to the advancement of a sustainable and resilient food system.
Sukrutha L. T. Vangipuram, Saraju P. Mohanty, Elias Kougianos
Consuming healthy food is one of the major concerns in the current and coming decades, as the agricultural food supply chain is confined to a lot of wastage during retail and unprepared storage acts. The conventional and zero tracking and communication systems towards their supply chain participants are some of the many reasons for damaged food delivery to consumers. With the aid of the Internet-of-Agro-Things (IoAT), the state of the food is being gathered at different supply chain stages to monitor and keep visibility of the agricultural product stored and transmitted. However, data tampering can be an issue with these IoAT devices as they are more prone to hacking and vulnerabilities, leading to data security and reliability problems. The current paper overcomes the traditional storage platform limitations in the supply chain. In this paper, we have collected the temperature and humidity data from the IoT-Edge device and sent the statistics directly to Distributed Ledger with Masked Authenticated Message (MaM) and called it agroString 2.0. Our previous work delivered the supply chain statistics temperature and humidity data through the private Blockchain Corda. Here in agroString 2.0, with the help of the distributed ledger, we bring aspects of data security into the supply chain domain with zero cost and faster transaction times for data.
This work explores the path to the convergence of virtual and real-world plants under the context of the metaverse. A mobile application named MetaPlant has been designed to offer a platform for learning about planting experiences. The application simulates the development and growth of individual plants based on the GreenLab model. It incorporates various features including the simulations of virtual fields and environmental conditions, the interactions with virtual plants, the visualization of plants using OpenGL, and user-to-user communications through the friend and community systems. This work can potentially integrate virtual and real-world farming practices using Internet of Things (IoT) technologies, produce digital products through Decentralized Autonomous Organization (DAO), and promote sustainable farming practices and knowledge sharing within a vibrant virtual farming community.
In agriculture, soil is a vital element that decides the quality and yield of agricultural produce. Soil consists of various nutrients such as nitrogen (N), phosphorous (P), potassium (K), the potential of hydrogen (pH), and water content. Nitrogen is responsible for building chlorophyll, which helps produce proteins and thus directly contributes to plant growth and development. Phosphorous is needed to develop root systems and flowers, whereas potassium helps increase disease resistance. Each of these play a role in crop cultivation. Thus, in this research paper, considering the fact that soil health will provide farmers with the best selection of crops that are compatible with their farm’s soil nutrients, we propose an algorithm for recommending a set of suitable crops based on various soil attributes. These soil nutrients can be collected in real-time using soil sensors, such as N, P, K, and pH, and humidity sensors. They can be deployed in farms where the cultivation takes place. These sensor readings would then be transferred to the blockchain layer, thereby validating the data and ensuring it is tamper-proof and evident. The crop recommendation model uses data from these sensors in real-time, increasing the results’ accuracy. The last stage leads us to display these results via a user dashboard, which helps the farmers to keep in check with their farm’s practices, and their sensor states from remote locations.