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.
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.
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 management—things 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 framework—Hyperledger Fabric—which 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 application’s business logic, automating rules for user-device interactions, data monitoring, and device management. The system’s 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 platform’s 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 solution’s superiority over centralized alternatives in scalability and resilience, particularly for resource-constrained IoT environments. By harmonizing IoT’s sensing capabilities with blockchain’s 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.
Antonio Villafranca, Igor Tasic, Victor Gallegos, Almudena Giménez · 7 authors
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 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.
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.
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.
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.
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.
This research presents a solution that combines deep learning-based image processing, blockchain technology, and the Internet of Things (IoT) to achieve smarter control and traceability in greenhouse operations within the agricultural sector. By integrating these technologies, the aim is to overcome challenges posed by climate change, plant growth, limited agricultural land, and water scarcity, while enhancing crop yields and ensuring efficient and secure operations. The proposed system automates image capture, measurement, storage, and monitoring of environmental parameters in greenhouses, utilizing highly accurate image processing techniques with a 98% success rate. The integration of blockchain technology establishes an immutable and transparent record of transactions and data points, thereby improving traceability across the agricultural supply chain. This comprehensive approach fosters accountability, transparency, and trust, empowering consumers to make well-informed decisions regarding the products they purchase. Ultimately, this research contributes to advancing efficient and sustainable agricultural practices.
Smart farming, as a branch of the Internet of Things (IoT), combines the recognition of agricultural economic competencies and the progress of data and information collected from connected devices with statistical analysis to characterize the essentials of the assimilated information, allowing farmers to make intelligent conclusions that will maximize the harvest benefit. However, the integration of advanced technologies requires the adoption of high-tech security approaches. In this paper, we present a framework that promises to enhance the security and privacy of smart farms by leveraging the decentralized nature of blockchain technology. The framework stores and manages data acquired from IoT devices installed in smart farms using a distributed ledger architecture, which provides secure and tamper-proof data storage and ensures the integrity and validity of the data. The study uses the AWS cloud, ESP32, the smart farm security monitoring framework, and the Ethereum Rinkeby smart contract mechanism, which enables the automated execution of pre-defined rules and regulations. As a result of a proof-of-concept implementation, the system can detect and respond to security threats in real time, and the results illustrate its usefulness in improving the security of smart farms. The number of accepted blockchain transactions on smart farming requests fell from 189,000 to 109,450 after carrying out the first three tests while the next three testing phases showed a rise in the number of blockchain transactions accepted on smart farming requests from 176,000 to 290,786. We further observed that the lesser the time taken to induce the device alarm, the higher the number of blockchain transactions accepted on smart farming requests, which demonstrates the efficacy of blockchain-based poisoning attack mitigation in smart farming.