Search Results (177)

Institutional adoption of blockchain technology in supply chains, healthcare, and public administration remains constrained. Organizations that manage digital assets on behalf of large numbers of non-technical users lack custody architectures suited to their scale. Existing approaches either require users to manage private keys directly; rely on centralized custodians that store encrypted keys; or depend on distributed protocols such as multi-party computation, which impose substantial infrastructure and coordination overhead. This paper presents CryptoVault, a stateless custody architecture for institutional blockchain deployments that derives private keys on demand from a single master seed using BIP-44 hierarchical deterministic (HD) wallets, eliminating persistent storage entirely. Only an AES-256-GCM-encrypted derivation index is persisted per wallet; the corresponding private key is re-derived at signing time and discarded immediately after use, ensuring no private key material ever rests on disk. The security model requires the simultaneous compromise of three independent components (the encrypted derivation index, the encryption key, and the master seed) for full key recovery, compared to two components in custody systems that persist encrypted private keys. An empirical evaluation under concurrent load demonstrates 13 to 22 ms steady-state signing latency on development hardware, with re-derivation accounting for approximately 4 to 7% of that total, confirming that on-demand derivation introduces negligible overhead. Thus, CryptoVault has been validated against an agricultural cooperative deployment as a representative institutional scenario, with an architecture that generalizes to any organization managing wallets on behalf of users who have no direct interaction with cryptographic material. A reference implementation is available as open-source software. Full article

Digital transformation and transparency in the seed supply chain are cornerstones of national food security and sustainable agricultural development. Existing agricultural traceability systems suffer from elevated storage overhead and performance degradation with massive seed data processing and fail to iterate quality supervision standards without disrupting continuous business operation. To address these problems, this study proposes a dual-optimization architecture-based traceability system for seed supply chains. An edge-assisted Merkle-tree dimension-reduction aggregation protocol is introduced to compress seed logistics scanning data before blockchain submission. Instead of storing each circulation record as an independent on-chain state update, the proposed scheme anchors one fixed-size Merkle root for each aggregated batch, reducing the per-batch on-chain payload to a constant size and lowering the overall on-chain anchoring burden from record-level growth to batch-level growth. Furthermore, it adopts a decoupled regulatory architecture based on the Strategy Pattern for the separation of traceability state storage and compliance inspection logic, enabling uninterrupted rule switching under the tested upgrade scenario via on-chain hash pointer adjustment. Rigorous statistical evaluation of the experimental results indicates that the system stably processes seed circulation records at a peak effective throughput of 1952.4 transactions per second. Under high-frequency concurrency, the 95th percentile (P95) latency remains controlled under 0.28 s. The average physical on-chain storage for 100,000 circulation records was reduced to 0.52 MB, and deploying a new quality inspection rule takes an average of only 2.2 s, with limited computational resource overhead. Full article

The agricultural sector is undergoing a profound digital transformation driven by artificial intelligence, the Internet of Things, remote sensing, robotics, blockchain, and edge computing, which are being integrated into crop monitoring, irrigation management, disease detection, and supply chain transparency systems. This study employs systematic evidence mapping to characterize the applications of emerging digital technologies in sustainable agriculture; it delineates technological trajectories, areas of application, implementation gaps, and opportunities for improvement. Adhering to the PRISMA 2020 reporting protocol, 101 peer-reviewed articles indexed in Scopus and Web of Science (2020–2025) were identified, screened, and subjected to integrated thematic and bibliometric synthesis, using RStudio Version: 2026.01.1+403 and VOSviewer 1.6.20 for data mining on keywords and technological evolution patterns. Results show that deep learning and computer vision models achieved diagnostic accuracies of 90–99%, smart irrigation systems reduced water consumption by 10–30%, predictive yield models frequently reported R² values above 0.80, and greenhouse automation reduced energy consumption by approximately 20–30%. Blockchain-based architectures improved traceability and secure data transmission by 15–20%, while remote sensing integration enhanced spatial estimation accuracy up to R² = 0.92. The findings demonstrate a measurable transition toward data-driven, resource-efficient agricultural ecosystems supported by validated digital architectures. However, interoperability limitations, lack of standardized performance metrics, scalability challenges, and uneven geographical implementation—identified in nearly 40% of studies—highlight the need for harmonized evaluation frameworks, cross-platform integration standards, and long-term field validation to ensure sustainable and scalable digital transformation. Full article

New regulations, such as the EU Deforestation-Free Regulation (EUDR), make verifiable agricultural data (AgData) essential for global trade. However, its value is compromised by a widespread “AgData Paradox”, characterized by distrust and fragmentation. To address this problem, we present AgriTrust, a federated semantic governance framework that automates and governs data sharing. Its key methodological innovation lies in the deep integration of a multi-sectorial governance model with a semantic digital layer, implemented through the AgriTrust Ontology (an OWL ontology for tokenization and traceability) and a multi-vendor, blockchain-agnostic architecture that avoids single-vendor dependence. We demonstrate the framework’s feasibility through simulated case studies in three critical Brazilian supply chains: coffee (EUDR compliance), soybean (mass balance), and beef (animal traceability). Using a semantic reasoning pipeline on a proof-of-concept federated knowledge graph of 2010 triples, we show how AgriTrust enables verifiable provenance representation, automated compliance checking via executable data contracts, and cross-platform asset management. The results provide initial evidence that AgriTrust offers a conceptually coherent blueprint for agricultural data sharing, though operational deployment, scalability testing, and performance validation under real-world conditions remain as future work. Full article

The agriculture sector plays a pivotal role in global economies, and optimizing its perishable food supply chain (PFSC) is vital to ensuring food security and transparency. The purpose of the study is to develop a blockchain-based smart contract to secure and provide transparency about perishable goods in the PFSC while delivering the goods between the stakeholders, such as farmers, mandis, and wholesalers. The study enhances collaboration between stakeholders by implementing smart contracts. The delivery status and the transactions have been safely recorded and verified by the stakeholder in the PFSC to ensure data integrity all the way through. The blockchain application has reduced fraud and streamlined the flow of goods and information. Moreover, this study emphasizes providing farmers with a straightforward route to the market to empower them. The benefits for the stakeholders are optimizing inventory control and developing appropriate decision-making skills. A three-echelon PFSC can become more resilient and is able to meet changing market demands by implementing blockchain-based smart contracts. Finally, the study employs blockchain technology to establish a decentralized and efficient PFSC, confirming a tamper-resistant system and enhancing stakeholder trust and collaboration. Full article

The agri-food sector is undergoing a profound transformation driven by the combined pressures of climate change, resource scarcity, policy frameworks, and evolving consumer expectations. In this context, digital and green technologies have emerged as key enablers of more sustainable, transparent, and resilient food systems. This review provides a comprehensive overview of the conceptual foundations, technological drivers, and policy frameworks shaping the digital and green transition of the agri-food sector. Digital technologies—including precision agriculture, sensing and data acquisition systems, artificial intelligence, blockchain, and data platforms—are examined in relation to their role in improving resource-use efficiency, traceability, and decision-making across the food value chain. In parallel, green technologies and sustainable practices in food production, processing, and waste management are discussed, with emphasis on resource optimization, circular economy approaches, and environmental impact reduction. This review further highlights the role of European and global policy frameworks, such as the European Green Deal and the Farm to Fork strategy, in steering technological adoption and aligning innovation with sustainability objectives. By synthesizing technological, environmental, and policy perspectives, this work underscores the importance of integrated digital–green strategies for achieving long-term sustainability, competitiveness, and resilience in agri-food systems. Full article

Under China’s rural revitalization and agricultural modernization strategies, digital village construction overcomes resource limits to drive transformation. Using 2013–2022 provincial panel data and a case study of Lin’an, Hangzhou, this study reveals how digital villages boost high-quality agriculture. The empirical results show they significantly enhance agricultural total factor productivity via three paths: IoT-driven precision production, blockchain-enabled green value addition, and e-commerce direct sales demonstrate more pronounced effectiveness in major grain-producing regions and those characterized by balanced production and sales. Simultaneously, this study employs the instrumental variable (TI) approach to address endogeneity from reverse causality and omitted variables. Mechanism testing reveals agricultural technological innovation exerts a significant 77.5% mediating effect. Finally, digital rural construction exhibits a non-linear threshold (0.3082); surpassing it triggers a gradual slowdown in growth with decreasing marginal returns. The Lin’an case validates the empirical results while revealing structural barriers, including industrial chain penetration gaps, data silos, and factor supply constraints, leading to the formulation of targeted optimization strategies. The practical contribution of this study is the proposal of a “data-value-technology” closed loop: public brands like “Tianmu Mountain Treasures” channel premiums into R&D funds, creating a self-sustaining mechanism. The findings indicate that digital villages drive high-quality agriculture primarily through direct effects, powered by full-chain tech coordination, institutional reform, and inclusive factor supply. Finally, this study proposes a coordinated governance framework encompassing “technical synergy, institutional innovation, and factor optimization,” providing theoretical support and strategic references for optimizing the pathways of regional agricultural digital transformation. Full article

As Unmanned Aerial Vehicles (UAVs) become integral to critical infrastructure, ranging from precision agriculture to emergency disaster recovery, their security becomes a matter of systemic resilience. This paper provides a comprehensive thematic survey of the security landscape for unmanned devices, bridging the gap between low-level hardware vulnerabilities and high-level mission failures. We propose a multidimensional taxonomy that categorizes challenges into hardware roots of trust, swarm intelligence threats, and domain-specific applications. A primary focus is placed on the Resource–Security Paradox, where the energy cost of heavy cryptographic or AI defenses directly reduces flight endurance, creating a trade-off that adversaries exploit through battery-exhaustion attacks. Beyond standard threats, we analyze emerging risks in additive manufacturing supply chains, the “Sim-to-Real” gap in AI-driven perception, and the legal necessity of Digital Forensic Readiness (DFR) for post-incident attribution. Through a systematic review of defensive frameworks, including lightweight encryption, Mamba-KAN anomaly detection, and blockchain-anchored logging, we evaluate the effectiveness of current solutions against complex adversarial models. Finally, we identify critical research gaps, providing a roadmap for security-by-design in the next generation of critical infrastructure swarms. Full article

The management of specialty coffee production represents a complex dynamical process characterized by highly nonlinear interconnections between environmental variables, agronomic practices, and chemical compositions. Traditionally, the classification of specialty coffee relies on sensory evaluations conducted by highly certified coffee experts named Q-Graders, using a strict, standardized Specialty Coffee Association (SCA) protocol. However, scientific methods that generate spectral fingerprints provide a more reliable guarantee of quality while also ensuring traceability to the farm of origin. Panamanian Geisha coffee is one of the world’s most expensive award-winning microlots, frequently exceeding 1000 American dollars per pound, with a record-breaking price of over 30,000 American dollars per kilogram in 2025. This research presents an integrated framework that combines Precision Agriculture Management Systems (PAMSs) and a traceability architecture that facilitates the collection of georeferenced coffee bean samples using a mobile application (apps), while preserving the coffee varieties and geographical origin necessary for the subsequent identification of the spectral fingerprint by chemical specialists in their laboratory. A mathematical model is introduced to formally characterize the mobile application’s behavior, distributed structure, and inherent constraints. Serving as a mathematical blueprint, this model identifies critical influencing factors and establishes strategic assumptions to distill complex real-world variables into a rigorous, manageable framework. Large-scale experiments conducted across more than 820 coffee farms in Chiriquí, Panama, demonstrate that the proposed decentralized architecture effectively coordinates the acquisition and synchronization of georeferenced chemical data. The decentralized architecture of the mobile application utilizes private blockchain technology to facilitate autonomous operations, effectively decoupling the system from central authorities to ensure functional continuity in environments characterized by intermittent connectivity. Full article

This study presents a systematic review of the role of key technologies in advancing sustainable logistics and supply chain management. Specifically, it explores the integration of Industry 4.0 (I4.0), logistics 4.0, and digital supply chains, focusing on technologies such as artificial intelligence (AI), augmented reality (AR), big data analytics (BDA), blockchain, cloud computing (CC), industrial internet of things (IIoT), machine learning (ML), robotics, virtual reality (VR), and internet of things (IoT). The aim is to examine how these technologies contribute to green logistics (GL), green supply chain management, sustainability, and the circular economy (CE). Data were collected from the Scopus database, covering studies published between 2019 and 2024. A total of 1471 publications were initially identified, and 39 studies met the selection criteria. The PRISMA approach was employed for the systematic review, revealing that leading research on I4.0 is concentrated in top-tier journals, with a significant number of publications from Italy focusing on digitalization in the agriculture and food sectors. Systematic literature reviews and resource-based theory are predominant, yet there is a notable gap in aligning research with the United Nations Agenda 2030 Sustainable Development Goals (SDGs). This paper provides insights into technological adoption trends and offers recommendations for industry leaders seeking to enhance sustainability, eco-friendliness, and alignment with the SDGs within their supply chains. Full article

Centralized agricultural data platforms raise concerns about ownership, provenance, and vendor lock-in, motivating decentralized alternatives. This study evaluates BigchainDB as a blockchain-database hybrid for owner-controlled precision agriculture data sharing. We address three research questions: (1) functional feasibility for data integrity, access control, and heterogeneous sensor integration; (2) integration patterns bridging IoT ingestion with blockchain consensus; and (3) operational trade-offs versus centralized alternatives. A proof-of-concept implementation comprising a sensor simulator, FastAPI middleware, and three-node BigchainDB cluster demonstrates end-to-end data flow with cryptographic provenance. Key contributions include the following: identification of three integration patterns (message queue buffering for high-throughput ingestion, hierarchical asset modeling, and dual-key access control); comparative analysis against five blockchain-database alternatives; and characterization of deployment complexity. Results show BigchainDB satisfies the functional requirements for data integrity and access control, while requiring increased operational overhead compared to single-node databases. The architecture is viable when multi-party governance outweighs operational simplicity, though production deployments require further scalability validation, including detailed performance benchmarking. Full article

Agri-food supply chains are highly exposed to freshness deterioration, demand uncertainty, and information asymmetry. In practice, upstream suppliers may strategically misreport freshness-related information to influence downstream procurement decisions, which can amplify inefficiency and increase food loss and waste. This study develops an analytical framework that integrates (i) strategic freshness misreporting by an informed supplier, (ii) endogenous investment in blockchain-enabled traceability that improves information credibility at a cost, and (iii) contract design for supply chain coordination. We consider a two-echelon agri-food supply chain with stochastic demand and freshness-dependent valuation, and characterize equilibrium operational decisions under centralized and decentralized settings. The results reveal how misreporting reshapes optimal order quantities, wholesale prices, and profit allocation, and identify conditions under which misreporting increases expected waste and undermines sustainability performance. We then examine how traceability investment changes the incentives of both parties, leading to adoption thresholds and potential incentive misalignment under decentralization. Finally, we design revenue-sharing, cost-sharing, and combined contracts and derive parameter regions that coordinate the blockchain-enabled agri-food supply chain and generate Pareto improvements for both the supplier and the retailer. Numerical experiments illustrate the comparative statics and quantify the trade-offs among profitability, transparency, and waste reduction. Relative to existing blockchain-enabled agri-food supply chain models, the framework jointly endogenizes supplier misreporting of freshness, blockchain-based traceability investment, and contract parameters, thereby uncovering new adoption thresholds and coordination regions that tightly link transparency decisions to food loss and waste. The findings provide actionable guidance for using digital traceability and contract mechanisms to curb opportunism, enhance coordination, and support sustainable agri-food supply chains. Full article

Blockchain technology can enhance traceability and trust in contract farming supply chains, yet high implementation costs deter adoption by supply chain participants. This study examines the synergistic mechanisms between blockchain adoption strategies and government subsidy policies. We develop a multi-stage Stackelberg game model involving an agricultural enterprise, an e-commerce platform, and a government, and comparatively analyze six decision-making scenarios across non-subsidy, unilateral subsidy, and full-chain subsidy settings. Three key findings emerge. First, blockchain investment has a cost–effect threshold below which consumer traceability preferences do not translate into profit gains. Second, well-designed subsidies overcome investment inertia and yield Pareto improvements in both profits and social welfare, with the full-chain subsidy model (Model BG) maximizing social welfare; however, subsidies exhibit distinct efficiency boundaries, and over-subsidization causes resource misallocation. Third, both supply chain parties tend to free-ride on the other’s investment, creating strategic conflicts that necessitate differentiated subsidy mechanisms tailored to specific dominance structures. These findings provide policy guidance for facilitating agricultural digital transformation and enhancing supply chain coordination. Full article

This study stems from the clear need to understand why and how organizations in Romania integrate emerging digital technologies into circular economy (CE) practices, given the critical role of this integration in improving resource efficiency and supporting sustainable business models. Data were collected through a structured questionnaire applied to 149 organizations of different sizes, ranging from SMEs (fewer than 50 employees) to large corporations (over 500 employees), operating across multiple sectors, including agriculture, construction, security, services and research. The questionnaire assessed organizations’ familiarity with CE principles, their stage of CE implementation, and their adoption of digital technologies, including artificial intelligence (AI), Internet of Things (IoT), blockchain, cloud computing and robotics. The results indicate that most organizations are aware of the potential benefits of digital technologies, particularly in terms of resource efficiency, enhanced product traceability and support for sustainability goals. However, effective implementation remains quite limited in many cases due to inadequate or outdated infrastructure, lack of technical skills, and organizational resistance to changes. At the same time, the findings further reveal a growing strategic interest in digitalization: approximately 41% of SMEs and 59% of large organizations plan to increase investments in digitalization, primarily to improve sustainability performance and foster innovation. Overall, the study provides a comprehensive overview of the current state of digitalization in support of CE in Romania and proposes practical recommendations for organizations and decision-makers, highlighting both emerging opportunities and persistent barriers. Full article

Background: Explainable Artificial Intelligence (XAI) is deployed in Internet of Things (IoT) ecosystems for smart cities and precision agriculture, where opaque models can compromise trust, accountability, and regulatory compliance. Objective: This survey investigates how XAI is currently integrated into distributed and federated IoT architectures and identifies systematic gaps in evaluation under real-world resource constraints. Methods: A structured search across IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Google Scholar targeted publications related to XAI, IoT, edge/fog computing, smart cities, smart agriculture, and federated learning. Relevant peer-reviewed works were synthesized along three dimensions: deployment tier (device, edge/fog, cloud), explanation scope (local vs. global), and validation methodology. Results: The analysis reveals a persistent resource–interpretability gap: computationally intensive explainers are frequently applied on constrained edge and federated platforms without explicitly accounting for latency, memory footprint, or energy consumption. Only a minority of studies quantify privacy–utility effects or address causal attribution in sensor-rich environments, limiting the reliability of explanations in safety- and mission-critical IoT applications. Contribution: To address these shortcomings, the survey introduces a hardware-centric evaluation framework with the Computational Complexity Score (CCS), Memory Footprint Ratio (MFR), and Privacy–Utility Trade-off (PUT) metrics and proposes a hierarchical IoT–XAI reference architecture, together with the conceptual Internet of Things Interpretability Evaluation Standard (IOTIES) for cross-domain assessment. Conclusions: The findings indicate that IoT–XAI research must shift from accuracy-only reporting to lightweight, model-agnostic, and privacy-aware explanation pipelines that are explicitly budgeted for edge resources and aligned with the needs of heterogeneous stakeholders in smart city and agricultural deployments. Full article