The most important problems of sustainable vegetable farming are pests, inefficient data-based decision making, and poor irrigation. Another threat to productivity and the environment is that the conventional methods will cause excess use of pesticides, over-irrigation and unreliable harvest. In this research, we suggest a Blockchain Ethereum-Backed IoT Platform to ensure that these problems are resolved and provide pest detection in real-time and smart irrigation control. IoT sensors are used to measure soil moisture, temperature, and humidity and edge devices with lightweight deep learning models can detect pest infestations with a high accuracy. The resulting data is encrypted and checked in the Ethereum blockchain smart contracts are used to run irrigation programs and send alerts to control pests. It uses Layer-2 solutions of Ethereum to reduce latency and transaction cost to achieve scalability and efficiency. It is scientifically proven that the proposed platform can detect pests with an accuracy of 93.8 %, use 35 % less water, and produce 20% more crops than traditional solutions. Moreover, surveys of farmers show that the level of trust and readiness to implement solutions based on blockchain has risen considerably. The contribution of this work is a secure and transparent and resource-efficient digital agriculture framework enabling the development of precision-based farming and supporting sustainable food production.
The emergent high-speed growth of precision agriculture requires solid frameworks that will improve sustainability, efficiency, and transparency in managing vineyards. The proposed research suggests a system that uses IoT sensor networks, machine learning models, and Ethereum-based blockchain to solve two important problems: pest identification and the optimization of water resources. The IoT layer was used to implement soil moisture, climate, and imaging sensors to gather real-time data about the vineyard. At the edge, preprocessing and deep learning algorithms were used with a blockchain-enabled Convolutional Neural Network (CNN) to provide correct pest detection. At the same time, the timing of irrigation was automated by IoT-enabled soil moisture monitoring, which greatly decreased the amount of wasted water. This ensured integrity of data and trust in the farmers as the Ethereum blockchain layer offered immutable storage, smart contracts to make decisions, and secure events logging. The results of the experiments revealed that the proposed system had a 95.8% pest detection accuracy, which was higher than the traditional and baseline machine learning methods. The efficiency of water management increased too by reducing water consumption by 55.2 % and doubling the crop productivity by 26.8 %t. Moreover, the blockchain application has scored high security of 0.93, confirming that it is a reliable solution even with moderate latency overhead. In this study, the authors emphasize the opportunities of converging the IoT with blockchain and transforming viticulture through sustainable practices, data safety, and efficiency.
The pests and the ideal irrigation should be monitored simultaneously so that the crops can be efficiently managed to yield the maximum. The intended dual-purpose solution to the pest detection problem, which is proposed in this study, is the combination of IoT-enabled sensors with Ethereum smart contracts and a deep learning-based ResNeSt-DDETR pest detector. The ResNeSt backbone is able to improve the extraction of features with the help of split-attention mechanisms, whereas Deformable DETR pays attention to the areas which are of interest in order to achieve precise detection in the field under complex conditions. IoT sensors constantly check soil moisture, temperature, and humidity to adjust the irrigation patterns to control the water management accurately. The pest detections and irrigation logs are registered safely on the Ethereum blockchain and provide a solution with tamper-proof, transparent, and traceable data. The system is deployed on edge devices and implemented on Python with PyTorch, OpenCV, and Web3.py and works in real time. The experimental assessment of the IP102 data reveals that the model has a high accuracy (95.2%), precision (94.5%), recall (93.7%), F1-score (94.1%), and mAP 0.5:0.95 = 90.6% indicating that it is effective in integrated pest and irrigation management in precision agriculture.
Pest detection and soil moisture estimation models with little computation overhead are needed in resource-efficient pesticide monitoring of agricultural fields. This paper introduces a lightweight MobileViT-based system that is combined with IoT sensors and a hybrid Ethereum blockchain platform to offer secure and real-time pest and soil monitoring. MobileViT is a hybrid architecture that uses convolutional networks and transformer-based global features, which allow competition with detection accuracy and low computing needs. The IoT sensors are used to monitor soil moisture, temperature, and humidity to aid in making irrigation decisions with the key events being safely stored on the Ethereum blockchain to trace the events irrevocably. The system is executed in Python using PyTorch, OpenCV, and Web3.py and runs on edge devices and is fast in inference with low latency. The IP102 dataset includes the evaluation which proves that the model has high detection performance with accuracy 92.8%, precision 91.5%, recall 90.7%, F1-score 91.1%.
Munir A. ADEWOYE, Ahmed Aliyu, Usman Ali, Abdulrasheed Jimoh
Food is fundamental to human survival, we eat to live, sustaining ourselves with nutrition that meets our daily needs. Food security, defined as universal physical and financial access to safe and nutritious food, depends heavily on efficient supply chains. However, ensuring this security faces significant challenges in tracking and transparency. This study examines two critical problems in blockchain-based food supply chain tracing: privacy preservation and scalability. While blockchain technology combined with Internet of Things (IoT) devices offers promising solutions for real-time monitoring, transparency, and fraud prevention in agricultural supply chains, questions remain about balancing computational efficiency with privacy protection, achieving scalable integration across multi-actor supply chains without compromising traceability, and implementing these systems in resource limited environments. Through a comprehensive review of current research, this study identifies emerging technologies like Zero Knowledge Proofs (ZKPs) and ZK-Rollups that enhance both throughput and privacy in decentralised systems. The research presents layered architectural models integrating blockchain ledgers, off-chain storage, IoT sensors, and cryptographic protocols to enable secure and scalable traceability. These models support compliance verification while protecting sensitive data and can be adapted for low-resource contexts. The findings demonstrate that scalable, privacy-preserving blockchain technologies can transform agricultural traceability, empowering supply chain stakeholders while maintaining data confidentiality and integrity. The study also identifies future research needs, including cross-chain interoperability, policy integration, cost-benefit analysis for smallholder farmers, and field validation.
Agriculture is constantly struggling with pests, water shortages, and poor management of other resources, and these directly affect crop production and sustainability. Conventional pest surveillance and irrigation techniques typically boil down to manual detection and programmed actions, which result in delays in reactions, waste, and an overuse of pesticides. This study offers a hybrid platform of the Internet of Things (IoT) and Ethereum blockchain that can be used to conduct real-time pest surveillance and optimize irrigation intelligently to address these constraints. IoT devices such as soil moisture sensors, environmental sensors, and pest detectors are camera based and collect and send real-time field information. Ready-to-use data are safely kept on the Ethereum blockchain, which ensures permanence, transparency, and inaccessibility to manipulation. Smart contracts are used to automate irrigation and provide pest control alerts at pre-defined thresholds, minimizing human intervention and increasing responsiveness. Experimental analysis shows great efficiency in irrigation (saving up to 24% of water), pest detection of 92% accuracy, and data integrity 99% impervious to tampering. IoT and blockchain integration also improve the level of productivity, as well as sustainability through reduced wastage of resources and creation of trust among the involved parties. This paper illuminates the role of decentralized, data-driven platforms in promoting climatesmart, and resilient agriculture.
Modern precision agriculture depends on safe and effective fertilizer management. However, existing systems lack real-time decision-making capabilities, rarely incorporate secure traceability methods, and mainly concentrate on nutrient prediction without determining the type of soil fertilizer utilized for a specific crop. To classify fertilizer types (organic vs. inorganic) in real-time based on soil nutrient parameters (temperature, pH, EC, N, P, and K), this investigation suggests an innovative, lightweight self-attention transformer neural network (TNN) based Fertilizer class contract network (FCCN) model. The proposed research is one of the first to combine secure blockchain recording, fertigation, and fertilizer-type detection into a single edge-based pipeline that operates in real time. The process integrates blockchain-based transaction logging and IoT-edge computing for recording transparent and secure agricultural activity. Whenever deficits emerge, the suggested method uses Venturi irrigation to automatically activate fertigation after processing real-time sensor data at the edge to determine the types of fertilizer utilized and the nutritional status. This work uses a decentralized and scalable architecture compared to cloud-dependent or AI-based-only models. Fertilizer classification and fertigation actions based on the real-time nutrient level recommendation are recorded as immutable transactions on an Ethereum blockchain using a Proof-of-Stake (PoS) consensus. Before the final on-chain recording, validator logic confirms the accuracy of field data, fertigation events, and real-time soil nutrient levels. Real-time blockchain measurements reveal transaction completion speeds of less than 0.03 seconds, gas consumption of less than 62,000 units, and throughput of 15-35. Experimental findings show that FCCN categorization accuracy surpasses 98.85%.
The Nigerian agricultural financing ecosystem, particularly incentive-based risk-sharing schemes such as the Nigeria Incentive-Based Risk Sharing System for Agricultural Lending (NIRSAL), faces persistent challenges related to data integrity, transparency, and trust among stakeholders. Centralized information systems expose sensitive financial and operational data to risks including unauthorized modification, fraud, lack of auditability, and single points of failure. This study proposes a blockchain-based information security framework designed to enhance transparency, integrity, and accountability in incentive-driven agricultural lending systems. The framework leverages distributed ledger technology, cryptographic hashing, consensus mechanisms, and permissioned access control to ensure tamper-resistant record keeping and secure transaction validation. A conceptual system architecture is developed to demonstrate how lending data, incentives, and risk-sharing records can be securely managed in a decentralized environment. The proposed approach improves trust among financial institutions, regulators, and agricultural stakeholders while reducing fraud, operational inefficiencies, and information asymmetry.
Aqsa Rashid, Raja Wasim Ahmad, Mirna Nachouki, Atta Ur Rehman Khan
Ensuring food safety and traceability in fruit supply chains (FSC) remains a critical concern, as traditional centralized methods often suffer from data manipulation, lack of transparency, and delayed responses during contamination events. These challenges lead to reduced consumer trust and inefficiencies in monitoring product integrity throughout the supply network. To address these limitations, this paper presents a blockchain-based framework that leverages cryptographic protocols and smart contracts to secure, automate, and validate traceability processes across all stages of the fruit supply chain. The proposed FSC_SDG system enforces trusted data recording, real-time provenance verification, and autonomous policy execution, while aligning with the United Nations Sustainable Development Goals (UN-SDGs). A proof-of-concept prototype was implemented on the Ethereum blockchain to assess performance. Experimental evaluations demonstrate reduced latency in traceability verification, improved data integrity, and enhanced resistance to tampering compared with existing approaches. These results confirm the effectiveness of the proposed framework in strengthening food safety, transparency, and trust within fruit supply chains.
R. N. V. Jagan Mohan, Pravallika Sree Rayanoothala, R. Praneetha Sree
Agriculture faces multifaceted challenges including climate variability, soil degradation, and supply chain inefficiencies, particularly for smallholder farmers practicing multicropping. This study systematically integrates blockchain technology for secure, transparent transactions with reinforcement learning (RL)-optimized Neutrosophic multi-regression for precise crop loss prediction in multicropping systems. Using real-world data from six crops (rice, banana, turmeric, elephant foot yam, coconut, cocoa), Neutrosophic multi-regression estimated losses with RL hyperparameter tuning, achieving superior prediction accuracy. A blockchain framework was developed for farmer validation, transaction security, and smart contract execution using Ethereum/Ganache. Results demonstrate 25%–35% reduction in predicted crop losses and enhanced supply chain traceability. This Smart Agriculture 5.0 framework advances Agriculture 4.0 through human-AI symbiosis and uncertainty modeling, addressing single-point failures, data privacy, and trust deficits for scalable sustainable farming Through this multidimensional approach, the study endeavors to not only enhance the productivity and sustainability of agricultural practices but also to foster resilience in the face of evolving challenges.
S. Shaffi Ahamed, M. Humera Khanam, Thatigutla Rohith Kumar Reddy, P. Sesha Saila Sree · 5 authors
Fragmented record-keeping across agricultural distribution networks enables unauthorized modifications, counterfeit products, and provenance erosion as data passes through multiple stakeholder tiers. Although distributed ledgers provide tamper resistance, direct on-chain storage imposes prohibitive costs and throughput constraints for production-scale systems. This paper presents AgriChain, a dual-layer architecture combining Ethereum smart contracts with InterPlanetary File System (IPFS) distributed storage for economically viable farm-to-consumer verification. Transaction fingerprints and cryptographic digests reside on-chain, whereas bulk documentation—quality certifications, logistics records, and environmental sensor data—resides off-chain in IPFS. End consumers validate product authenticity via QR-encoded identifiers that trigger real-time blockchain queries. The implementation employs AES-256 for data confidentiality, JWT tokens for stakeholder authentication, and SHA-256 for cross-layer integrity checks. Deployment on Ethereum Sepolia testnet yielded 42 percent lower transaction fees compared to full on-chain storage, maintained response times below two seconds, and achieved 99.7 percent availability across evaluation trials. Findings demonstrate that strategic partitioning of on-chain and off-chain components enables both auditability and economic feasibility in agricultural traceability infrastructure.
Dr.B.Lakshma reddy, Chetan Kalamadi, B GURURAJ, Dhruva dinesh naik
This research presents Agri Safe, an innovative framework combining distributed ledger technology with machine intelligence for agricultural property documentation. Agricultural sustainability depends heavily on clear property rights, yet existing documentation mechanisms suffer from vulnerabilities enabling unauthorized modifications. Our solution leverages immutable distributed ledgers to ensure record permanence while employing classification algorithms including support vector machines, logistic regression, and random forests for pre-validation fraud detection. This dual-layer approach prevents illegitimate entries from consuming computational resources. Validation rules embedded within automated contracts ensure operational efficiency and data integrity. The Inter Planetary File System handles supplementary documentation, with cryptographic hashes anchored on-chain for verification. Comprehensive evaluation demonstrates system reliability through AI model performance assessment and smart contract security analysis. Agri Safe establishes trust by eliminating unauthorized modifications and minimizing property conflicts.
ABSTRACT The perishable food cold chain is a vital part of the global food system, mainly ensuring the quality of temperature‐sensitive products such as milk, seafood, fruits, and vegetables. However, this system still faces several challenges related to real‐time monitoring, data transparency, and proactive demand planning, which often lead to spoilage, food safety violations, and disruptions. In this work, we propose a comprehensive end‐to‐end system that includes Internet of Things (IoT)‐based environmental sensors, blockchain‐enabled immutable logging, and a hybrid ARIMA–LSTM model for demand forecasting and spoilage risk detection. The system uses ESP32 microcontrollers with DHT11 sensors at key cold‐chain points to collect temperature and humidity data, streamed to Blynk dashboards for real‐time visualization and anomaly alerts. Anomalies and product metadata are permanently recorded through smart contracts on a private Ethereum blockchain, while payloads are stored off‐chain on IPFS for traceability and auditability. Additionally, historical blockchain logs and external sales data are used to train a hybrid ARIMA–LSTM model that predicts future demand and spoilage risks more accurately. Experimental results show that the proposed model achieves a forecasting accuracy of 90% ( R 2 = 0.90), outperforming baseline approaches on multiple metrics including RMSE, MAE, MAPE, and R 2 . The framework is scalable and data‐driven, aiming to improve supply availability and reduce food waste.
A new approach to influence longetivity of crops in a humid and heated area by bitcoin computers aimed to reduce greenhouse emmisions while sustaining food for all.
Rossana Caputo, Bernard Mallia, Keerthi Kumar Masanasetty, Prasad M
Agricultural digitisation is increasingly driven by regulatory compliance, sustainability mandates, and the need for trusted multi-stakeholder collaboration. However, agricultural equipment traceability remains largely manual or dependent on energy-intensive Internet of Things (IoT) infrastructures that are ill-suited for rural environments. This paper presents AgriLink, a privacy-preserving and energy-efficient Distributed Ledger Technology (DLT) framework for agricultural equipment and workforce traceability, developed under the TrustChain OC5 initiative. AgriLink introduces an event-driven architecture based on passive Near Field Communication (NFC), selective blockchain anchoring, zero-trust security, and privacy-by-design principles. By functioning primarily on episodic, event-driven, non-continuous data flows, the framework enables verifiable digital twins of physical agricultural assets without continuous telemetry or battery-powered sensors. This study details the system architecture, threat modelling, privacy mechanisms, and a comparative evaluation against traditional IoT tracking systems. Experimental validations indicate up to a 94% reduction in energy consumption at the sensing layer compared to an average of Bluetooth Low Energy (BLE), ZigBee, and LoRaWAN architectures, while maintaining robust auditability and General Data Protection Regulation (GDPR) compliance. The results establish AgriLink as a viable blueprint for sustainable DLT adoption in physical asset-intensive agricultural sectors.