Letters of Credit (LCs) are pivotal in global trade finance, yet traditional processes are plagued by inefficiencies, fraud, and a lack of transparency, particularly in developing economies such as Bangladesh. This study investigates how three technological innovations Blockchain Database Integration (BDI), Collaborative Platforms (COP), and Compliance Automation (CAU) drive the evolution of a Sustainable Letters of Credit Supply Chain (SLCSC), mediated through the development of a Technology-based LC Supply Chain (LCSC). Drawing on a sample of 400 LC stakeholders in Bangladesh, the research employed a quantitative methodology using a reflective measurement model. Data were analyzed using Exploratory Factor Analysis (EFA) in IBM SPSS and Structural Equation Modeling (SEM) in IBM AMOS to assess reliability, validity, and the hypothesized relationships. The results indicate that Compliance Automation exerts a strong, significant positive effect on the LCSC (? = 0.661, p < 0.001) and Collaborative Platforms a weaker but significant effect (? = 0.087, p = 0.04), whereas Blockchain Database Integration has no significant effect (? = 0.016, p = 0.687). The LCSC, in turn, exerts a strong positive impact on the SLCSC (? = 0.938, p < 0.001). The findings demonstrate that compliance automation is the primary enabler of a technology-based LC supply chain, while the influence of blockchain remains constrained by prevailing infrastructural and regulatory conditions. The study’s principal contribution is to disaggregate the technological drivers of trade-finance digitalization into three empirically distinct constructs and to demonstrate that their influence on sustainability is fully mediated by the technology-based LC supply chain, providing structural- model evidence of this mechanism from Bangladesh’s banking sector. The study offers critical insights for banks, businesses, and policymakers seeking to modernize LC operations for enhanced efficiency, security, and eco- efficiency.
Palm leaf manuscripts have rich sources of knowledge and information reflecting cultural, historical, and linguistic knowledge. Extracting information from palm leaf manuscripts poses significant challenges for preservation and access, as they are fragile in nature. We propose an advanced multimodal deep learning framework for the digitization, character reconstruction, and decentralized federated learning of palm leaf manuscripts. The proposed approach integrates Transformer-based OCR models (TrOCR, LayoutLM), Vision Transformers (ViTs), and Contrastive Language-Image Pre-training (CLIP) to enhance character recognition for damaged and missing characters in the manuscripts. Natural Language Processing Algorithms are implemented to restore incomplete or faded characters while preserving the originality of the manuscripts. To ensure secure and decentralized access, we employ a blockchain-based federated learning system where metadata, translations, and reconstructed text are securely stored on a Zero-Knowledge Proof (ZKP) blockchain ledger. Federated learning across distributed nodes minimizes the centralized dependencies while enabling real-time collaborative OCR model updates. Scalability is ensured by Docker and Kubernetes where real-time processing is done across distributed nodes. Experimental results demonstrate superior OCR accuracy (96.3%), improved character restoration fidelity (92.7%), and enhanced blockchain security with minimal overhead (4.2%), outperforming traditional methods. The proposed method stores the manuscripts in a digitized form, providing easy access for researchers and scientists globally. The proposed work unlocks the hidden treasures, knowledge and information from the cultural treasures. Results obtained show that the efficiency and scalability of the proposed approach paves a path into the digital era by enhancing cultural preservation.
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%.
Yahaya Saidu, Shuhaida Mohamed Shuhidan, Dahiru Adamu Aliyu, Izzatdin Abdul Aziz · 5 authors
The need for sophisticated traceability systems has become essential in increasingly complex and globalized supply chains. The convergence of Blockchain (BC), Internet of Things (IoT), and Artificial Intelligence (AI) technologies offers promising solutions to enhance traceability systems across various sectors, particularly supply chain management (SCM). This paper presents a comprehensive bibliometric and systematic literature review to explore emerging trends, research patterns, and methodologies in integrating BC, IoT, and AI into traceability systems. In the study, 530 documents from the SCOPUS database for bibliometric analysis were examined, alongside a detailed review of 43 selected articles from multiple databases. The findings highlighted a significant increase in research output in recent years, with a dominant focus on agricultural supply chains and SCM. Notably, India and China lead the field in publications and citations. Furthermore, key authors and influential journals significantly contributed to advancing the research. The analysis also revealed a predominance of experimental and hybrid research methodologies, with Ethereum and Hyperledger Fabric emerging as the most widely adopted platforms for system implementations. The study identified critical research trends, including the growing role of AI-driven analytics, the importance of real-time IoT data collection, and the critical need for secure, tamper-proof data provided by BC. However, challenges, such as interoperability, scalability, and standardization, remain hindering the widespread adoption of these technologies. The paper proposes a four-layer conceptual framework for integrating BC, IoT, and AI into future traceability systems, emphasizing their potential to enhance transparency, security, and efficiency across various application areas. The paper concludes by offering directions for future research, highlighting the need for more empirical studies, industry-specific frameworks, and standardization to overcome existing limitations.
This study presents a blockchain-based traceability system designed specifically for the olive oil supply chain, addressing key challenges in transparency, quality assurance, and fraud prevention. The system integrates Internet of Things (IoT) technology with a decentralized blockchain framework to provide real-time monitoring of critical quality metrics. A practical web application, linked to the Ethereum blockchain, enables stakeholders to track each stage of the supply chain via tamper-proof records. Key functionalities include smart contracts that automate quality checks, ensuring data integrity and providing immediate verification of product authenticity. Initial user feedback highlights the system's potential to enhance transparency and reduce fraud risks in the olive oil market, supporting consumer trust and regulatory compliance. This approach offers a scalable solution adaptable to other high-value agricultural products, demonstrating the blockchain's transformative potential for secure and transparent food traceability.
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.
One promising solution to address the multifaceted challenges encompassing the economic, environmental, and social sustainability aspects of the palm oil industry lies in harnessing spatial data. This valuable data is acquired through cutting-edge geospatial technologies such as remote sensing, GIS, and GPS. When geospatial data is coupled with other data collected by IOT devices, Artificial Intelligent (AI)-driven analysis, and distributed ledger technology such as block chain, innovative solutions can be created for comprehensive understanding, monitoring, and management of plantations to enhance palm oil yield and concurrently realize environmental and social sustainability goals.
In Tunisia, one of the major problems of the olive oil industry is marketing. Several factors have an impact, such as quality, originality, lobbying, subsidies and the certification of extra virgin olive oil. The major problem remains the traceability of the production process to guarantee the origin of the food at all times. This fine-grained traceability can be achieved by applying Blockchain technologies. Blockchain can be used as a solution that could bring visibility to the oil supply chain. It is proposed in order to guarantee the veracity of the product information at different stages. In this paper, a multi-Blockchain, multi-sensor traceability system using IoT will be presented. Two Blockchains that can be programmed via Smart Contract will be used. The first one is Quorum, which is a private Blockchain used by the actors of our system, and the second one is Ethereum, which is public and connects the different actors who have access to our system. This smart contract allows us to conta our system to track the olive oil manufacturing process from the farmer, through the oil mill, the transporter and the quality controller to the customer. A general approach for managing the olive oil supply chain is presented. This approach offers the possibility for the system to be configurable. It is based on smart contracts and applications that interact with the same smart contracts. The IoT is used to configure sensors. These sensors are the source of data for the supply chain process. These sensors are connected to the embedded platforms that host Quorum.