The fast-developing Industry 4.0 has brought about shifts into our food and nutrition systems with automation, data analytics and digital connectivity. This systematic review investigates how IoT, AI, robotics and blockchain interact with each other on the relation between food systems and safety, traceability and personalized nutrition. In accordance with the PRISMA framework, 68 studies (2013–2025) were synthesized across food production, processing and nutrition settings. Results indicate that Industry 4.0 technologies increase transparency, save 20-40% wastage, and make precision nutrition possible through predictive analytics but some obstacles exist such as cost, infrastructure and regulation. This review presents a cross-sectorial view of digital transformation in food and nutrition systems and identifies research priorities in the path towards human-AI collaboration, sustainable innovation in Industry 5.0.
Food Supply Chain Traceability
Smart Agriculture and AI
Agriculture Sustainability and Environmental Impact
The security, integrity, and trustworthiness of heterogeneous data have become a burning issue in the rapidly changing smart agriculture environment. This chapter discusses how machine learning (ML) and blockchain technologies can be integrated to ensure the security of agriculture-based applications in the Internet of Multimedia Things (IoMT). It explains how multimodal agricultural data, including sensor measurements, satellite pictures, videos taken by drones, and farmer feedback, can be smartly analyzed with the help of ML and deep learning algorithms and safely stored and shared with the help of blockchain systems. The chapter brings to the fore ML-based methods in detecting anomalies, predicting yields, detecting diseases, and decision support and blockchain capabilities of decentralization, immutability, smart contracts, and traceability. The proposals of the architectural models of ML-blockchain-based agricultural systems are introduced with a focus on secure data exchange, access management, and trust management. Practical use cases such as supply chain monitoring, precision farming, and sustainable resource management are also discussed in the chapter and end with the main challenges, limitations, and future research directions.
This chapter presents the concept of a Smart Rice Mill as an intelligent, connected, automated, and traceable rice-processing ecosystem. It integrates IoT sensors, computer vision, deep learning, Edge AI, cloud analytics, predictive maintenance, intelligent control, and blockchain to improve rice-processing operations. The chapter discusses automated grain inspection, variety classification, defect detection, broken-rice estimation, milling-quality prediction, machine monitoring, process optimization, and digital recording of batch history. It also examines implementation challenges involving legacy machinery, hardware and sensor reliability, cybersecurity, staff training, integration, and economic feasibility. The proposed future direction is a closed-loop Smart Rice Mill capable of sensing paddy and machine conditions, predicting quality, adjusting processing parameters, verifying output, and maintaining complete traceability.
This chapter proposes an integrated Blockchain–IoT–AI framework for secure and intelligent quality traceability, particularly in agricultural and rice supply chains. It explains how IoT sensors can continuously collect physical and environmental information, AI models can analyze images and sensor data for quality assessment, and blockchain can securely record important quality events and processing information. The framework supports unique digital identities for rice batches, quality monitoring, defect detection, moisture estimation, quality scoring, and QR-based access to traceability information. The chapter examines applications in rice quality certification, smart rice mills, food safety, warehouses, export-quality monitoring, consumer verification, and government procurement. Challenges related to data quality, sensor reliability, interoperability, stakeholder participation, scalability, and regulatory coordination are also addressed.
Abstract This study explores the role of Artificial Intelligence (AI) in transforming agricultural supply chain management in Bangladesh through a systematic comparative analysis of existing literature, institutional reports, and global case studies. AI technologies including predictive analytics, machine learning, blockchain, and precision agriculture are examined for their potential to address longstanding inefficiencies in Bangladesh’s agri-supply chain. The study finds that AI-driven demand forecasting models using LSTM and ARIMA achieved 89–92% crop yield prediction accuracy, representing a 37% improvement over traditional methods. Smart warehousing systems reduced operational costs by 25% and increased order processing speed by 40%, while blockchain integration cut payment cycles from 15 days to 2.3 days and increased smallholder farmer incomes by 22–25%. Precision agriculture technologies achieved 25% yield growth with 15–20% water savings and 30% fertilizer efficiency gains. Despite these promising outcomes, Bangladesh’s AI adoption rate remains at only 18%, significantly behind India (35%) and Vietnam (28%), primarily due to insufficient infrastructure, lack of digital literacy, and high implementation costs. The study proposes targeted policy interventions including IoT subsidies, farmer training programs, and public-private partnerships to enable inclusive and sustainable AI integration across Bangladesh’s agricultural sector.
Abstract A secure platform for exchanging and storing agricultural data is provided via a blockchain-powered framework. By integrating edge computing, blockchain technology, and the Internet of Things (IoT) the production of crops can be boosted while using fewer natural resources. In the sector of agriculture, sensors and equipment gather various data about the landscape, which can subsequently be delivered to a server in a cloud environment. Due to its extreme fragility, these data must be securely stored and guarded from unwanted access. The core aim of this work is to propose a hybrid Reconditioned Random value-based Wombat Optimization with Adaptive Multi-scale Vision Transformer-based EfficientNet (RRWO-AMViT-ENet) model integrated with Ethereum smart contracts for secure pest detection and smart irrigation in IoT environments. The gathered agricultural images are initially stored and managed using the Ethereum blockchain. Then, node authentication is performed using the Smart Contract-based Adaptive Deep Support Vector Machine (SC-ADSVM). A Reconditioned Random value-based Wombat Optimization (RRWO) is utilized to optimize the variables of the developed SC-ADSVM. In order to perform pest detection and smart irrigation, the Adaptive Multi-scale Vision Transformer-based EfficientNet (AMViT-ENet) is used. The proposed model is implemented on the IP102-Dataset, where it obtained an accuracy of 96.39% in the pest detection operation. Thus, the proposed model provides effective results for pest detection and the smart irrigation process. From the attained results, it is concluded that the recommended strategy can provide intelligent service to the farmer.
The global dairy industry confronts a persistent structural challenge in operationalising food safety and animal welfare compliance. Manual inspection regimes and intermittent audits are demonstrably inadequate for the heterogeneous, geographically dispersed landscape of small-scale farming, where data integrity, real-time monitoring capability, and regulatory transparency are simultaneously compromised. This article presents GreenDairyChain, an integrated compliance innovation framework that synthesises four enabling technologies: GreenEdgeML (a lightweight TinyML inference engine optimised for microcontroller-class devices), Privacy-Preserving Federated Learning (FL) with Graph Attention Network (GAT)-based dynamic clustering, Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) for cryptographic compliance verification, and a Layer-2 Polygon zkEVM Blockchain with domain-specific smart contracts governing farm identity, violation detection, audit triggers, and licence management. GreenEdgeML executes multimodal sensor fusion across four signal modalities (body temperature, accelerometer activity, ammonia concentration, and milk pH) entirely on-device using 8-bit integer quantisation, consuming 64.6 KB RAM and 82.7 mW per inference cycle on the ESP32 platform. The FL engine employs GAT-based farm clustering with DBSCAN outlier exclusion to address non-IID data heterogeneity while maintaining Byzantine fault resilience. Compliance inferences are encoded as R1CS arithmetic circuits (14,240 constraints) and verified on-chain at O(1) cost through ZK-SNARK proofs generated in 1.25 seconds. Evaluated on the Shahhet28121 benchmark dataset across 16 biomarkers, the full system achieves 96.94% global classification accuracy, a 97.7% reduction in per-round communication payload (4.25 KB), and maintains classification accuracy above 90% under 20% Gaussian sensor noise. Ablation experiments confirm that each architectural component contributes independently to system performance. The findings carry implications for green business innovation, sustainable agriculture governance, and the design of trustworthy AI ecosystems in resource-constrained rural contexts.
This research discusses the incorporation of IoT with blockchain technique to enhance the efficiency of smart farming systems, particularly focusing on plant disease classification, pest detection, and smart irrigation. The study aims to develop a secure and effective IoT-based smart farming framework using the Ethereum blockchain to store and transmit data, and a Hybrid Convolution Adaptive Recurrent MobileNet (HC-ARMNet) model for predictive analytics, optimized by the Improved Secretary Bird Optimization (ISBO) algorithm. The research employs IoT sensors to acquire real-time data, which is then stored in the Ethereum blockchain to ensure security. The HC-ARMNet model, combining 1D/2D convolutions with recurrent connections, processes this data for pest detection and irrigation management. The ISBO algorithm is leveraged to fine-tune the technique's parameters. Datasets used: The proposed system utilizes three standard datasets for evaluation. The PlantifyDr Dataset is used for classifying plant disease, and the Pest Detection Dataset is used for recognizing pests. Also, for the smart irrigation process, the significant field images are collected manually. The accuracy, precision, and FNR rates of the ISBO-HC-ARMNet-aided plant disease classification are 94.16%, 94.2% and 5.87%. At the same time, the ISBO-HC-ARMNet-based pest detection process's accuracy, sensitivity, and specificity are 93.78%, 93.79% and 93.76%, respectively. In addition, the ISBO-HC-ARMNet-based smart irrigation task's MSE is 3.21, SMAPE is 0.03, and MASE is 30.23. Thus, the designed system showcases promising performance over classical approaches in terms of accuracy and error rates for plant disease classification, pest detection, and smart irrigation. The research concludes that the IoT-aided smart farming framework with blockchain and the HC-ARMNet model provides a robust solution for secure and efficient agricultural management. The system's predictive capabilities provide accurate and timely data analysis, facilitating to the improvement of precision agriculture. Future work will focus on improving the system with advanced feature extraction strategies to reduce processing time.
With the help of modern technologies, the agriculture industry is undergoing a revolution that appears to hold great promise for advancing plant production and profitability. Over the past decade, the continued development of Agriculture 4.0 has expedited the digitization of agriculture. The integration of blockchain technology with artificial intelligence (AI) has great potential to address plant leaf diseases and transform agricultural methods in the era of Agriculture 4.0. Additional uses of AI in agriculture include agricultural decision support systems, mobile agricultural expert systems, and agricultural predictive analytics, which incorporate classification and decision-making skills. Blockchain guarantees transparency, data integrity, and secure information transmission, while artificial intelligence (AI) and blockchain-based aloe vera plant leaf disease detection systems may be able to use learning to provide precise diagnoses and suggestions. The role of blockchain technology and artificial intelligence in aloe vera plant leaf detection systems is examined in this study. AI models catalogue aloe vera leaf diseases and measure severity. However, blockchain systems like Ethereum 358 and Hyperledger Fabric use consensus processes called proof of authority and proof of stake to guarantee safe data logging. This decentralized approach inhibits data tampering, ensuring reliability and transparency in aloe vera disease detection records. By improving traceability, facilitating information exchange, and protecting privacy, the system supports stakeholders in datadriven agriculture management and contributes to farmers’ access to real-time insights.
Abstract Food security and the stakeholders’ trust are essential to ensure that agricultural supply chains are transparent and secure. This research presents a Queueing-Assisted Blockchain Smart Contract (QABSC) framework to enhance end-to-end traceability in the millet supply chain. The framework incorporates fog computing into real-time data processing to reduce latency and optimizes transaction flow using queueing techniques, thereby ensuring an efficient and scalable blockchain supply chain platform. The Internet of Vehicles and Things (IoVT) connects cars, sensors, roadside infrastructure, and cloud and edge technologies to make transportation and mobility smarter. By integrating fog-layer intelligence with blockchain-based immutable record-keeping, Internet of Vehicles and Things enabled sensing and vehicular logistics, and end-to-end visibility, the proposed system may ensure tamper-resistant monitoring of millet products from farms to customers. Internet of Things (IoT) sensors collect real-time information about millet quality and storage conditions. This data is securely stored using the InterPlanetary File System (IPFS) and verified by smart contracts on a distributed ledger. This approach ensures automated compliance verification for auditors and regulators, immutable data storage, and conditional privacy. The proposed model reduces bottlenecks in blockchain transaction processing and enhances efficiency, privacy, data integrity, and trust among producers, distributors, retailers, farmers, and buyers. The proposed model is evaluated based on key performance metrics. The experimental evaluations of the proposed framework demonstrate enhanced throughput, improved transparency, reduced computational overhead, and robust security. This research focuses on a unique integration of smart contracts, queueing theory, IPFS, Fog Computing, IoT devices, and blockchain technology to promote sustainable and transparent millet supply chain management.
This technical paper presents the architectural foundations and technological innovations of the GEORGIA platform. The publication introduces a decentralized, cloud-native cyber-physical system that integrates biophysical modelling, Explainable Artificial Intelligence, Zero-Trust Federated Learning, and Distributed Ledger Technology to support precision irrigation and drought resilience across diverse European agricultural systems.
Muruganantham Angamuthu, Safeyah Tawil, Gaurav Pushkarna, M. D. Boomija · 6 authors
Smart agriculture transforms food security, climate change, and resource optimization. In agriculture, IoT monitors soil, crop, irrigation, and supply chain operations in real time. As data-driven farming expands, stakeholders face security, transparency, trust, and interoperability issues. Centralized agricultural data management systems are unreliable due to tampering, illegal access, and single points of failure. Smart agriculture uses blockchain technology for secure crop tracking and data sharing via distributed ledger systems. The suggested architecture enables data immutability, traceability, and transparency across the agricultural lifecycle with IoT-enabled sensing and blockchain-based data storage and validation. Secure crop origin, cultivation, and logistics information is available to farmers, distributors, regulators, and consumers. Blockchain-supported smart agriculture enhances stakeholder trust, fraud reduction, and decision-making efficiency, studies show. The decentralized design allows resilient data exchange without centralization. We found that blockchain and IoT can modernize agricultural ecosystems, promote sustainable farming, and improve food supply chain integrity. This research develops secure, transparent, and intelligent digital agriculture systems.
Agricultural trade in developing countries continues to depend on informal agreements, multi-tier intermediary networks, and centralized payment mechanisms, resulting in payment delays of 30–90 days, information asymmetry, and weakened bargaining power for smallholder farmers. This paper presents AgriHandshake, a blockchain-based smart contract platform enabling direct crop trading between farmers and vendors through an automated escrow payment mechanism deployed on the Ethereum network. The system employs a hybrid architecture that stores cryptographic state hashes and escrow logic onchain while offloading trade metadata and delivery documentation to the InterPlanetary File System (IPFS), reducing average transaction gas costs to approximately 85,000–210,000 gas units per operation. A Solidity-based escrow contract enforces a structured four-state machine (CREATED→FUNDED→DELIVERED→COMPLETED/DISPUTED) with a 72-hour automatic payment-release timer implemented via block.timestamp. Experimental evaluation on the Ethereum Sepolia testnet demonstrates average smart contract function execution latency under 15 seconds, end-to-end trade confirmation within 3–8 minutes including IPFS upload, and strong resistance to reentrancy and front-running attacks. Comparative analysis against eNAM, FarMarket, and AgriOnBlock confirms that AgriHandshake is the first platform to combine a dedicated escrow payment guarantee, decentralized off-chain storage, and a farmer-centric usability model within a single deployable framework
The integration of Internet of Things (IoT) technologies into precision agriculture has transformed farming practices through real-time monitoring, automated irrigation, and data-driven decision-making. However, centralized access control mechanisms remain vulnerable to single points of failure, scalability issues, and security breaches such as spoofing and replay attacks. To address these challenges, this paper proposes a decentralized blockchain-based framework for access management in agricultural IoT environments. The architecture integrates IoT devices, edge gateways, smart contracts, and a distributed ledger to provide secure identity management, automated authorization, and immutable audit trails. Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) are combined to enable context-aware authorization, while Zero-Knowledge Proofs (ZKPs), Multi-Factor Authentication (MFA), and end-to-end encryption strengthen resilience against identity theft and unauthorized access. Performance evaluation demonstrates a$\mathbf{6 5 \%}$reduction in authentication latency and an access control accuracy of 98.5 %, confirming the framework's scalability and security effectiveness for large-scale precision agriculture deployments.
By making smart farming and precision agriculture truly revolutionary, the intersection of Artificial Intelligence of Things (AIoT) and blockchain technology has enabled safe, transparent, and intelligent systems of food production. The major problem in this field is to provide a credible authentication of heterogeneous devices, sensors, and stakeholders and guarantee the integrity and privacy of data. The chapter discusses how blockchain can be implemented together with decentralized identity (DID) systems to provide strong, unaltered authentication systems to AIoT-based agricultural ecosystems. The given approach helps to remove single points of failure, increase accountability, and allow farmers to have a better opportunity to control the ownership and sharing of data by leveraging the distributed ledger technology. Centralized identity promotes cross-agricultural device interoperability, stakeholders in the supply chain, and service providers. The chapter offers a conceptual framework, explains the implementation issues of scalability and energy efficiency.
The agri-food sector is an essential pillar of economies worldwide, playing a vital role in food security and safety, as well as the livelihoods of millions. However, it continues to tackle recurrent issues such as supply chain inefficiencies, lack of transparency, and simple food fraud. Smart contracts, a form of blockchain technology capable of executing programmable agreements between parties, are a promising remedy for these challenges. A smart contract is basically software that has contractual rules written into it. This chapter lays the foundation for smart contracts operating on a blockchain-based architecture, explains how they differ from regular contracts, and outlines the features that make them unique. In agribusiness, platforms like Ethereum and Hyperledger serve a crucial role in the implementation of smart contracts. Key applications include improving traceability, automating supplier-buyer interactions, and strengthening food safety and quality control. Real-world implementations illustrate their effectiveness in preventing fraud and ensuring compliance with industry standards. Despite their potential, the adoption of smart contracts in agribusiness is influenced by various factors, as analysed through Rogers' diffusion of innovation framework. Comparative advantage, compatibility, and complexity play pivotal roles in determining adoption rates. Case studies showcase successful implementations while shedding light on adoption challenges. Barriers to widespread use include technological constraints, regulatory uncertainties, infrastructure costs, and knowledge gaps, particularly among small-scale farmers. Additionally, data security and privacy concerns remain significant obstacles. Addressing these challenges is essential for harnessing the full potential of smart contracts in agribusiness. This chapter provides insights into overcoming these hurdles and fostering a more transparent and efficient agri-food ecosystem.
Huda M. Elmatsani, Arief Sartono, S. Joni Munarso, Sari Intan Kailaku · 14 authors
Background Agricultural supply chains are characterized by high transaction costs and agency risks stemming from information asymmetry and biological variability. Although blockchain is widely proposed as a solution, existing literature predominantly focuses on passive traceability rather than active algorithmic governance. Methods This study conducts a bibliometric synthesis of 367 documents (2018–2025) to map the field’s intellectual structure and research orientation. Co-occurrence analysis was employed to reveal distinct thematic clusters and identify the evolution of technological infrastructure in the sector. Results The analysis reveals a critical volume-impact paradox within the technological infrastructure group and a 16:1 asymmetry between traceability and automation research. This indicates a significant gap in leveraging smart contracts for economic enforcement and active supply chain management. Conclusion We propose the Agri-Cognito framework, a prescriptive architecture designed to bridge the cognitive void through AI-driven pre-consensus validation. The framework provides a theoretical blueprint for transitioning agricultural blockchains from passive digital logbooks to autonomous governance ecosystems, offering a direct response to the “oracle problem” and structural inefficiencies in current implementations.
Abstract Decentralized Web Applications (dApps) built on blockchain, Web3 technologies, and the Inter- Planetary File System (IPFS) are emerging as a promising solution to longstanding challenges in agriculture. Conventional centralized systems often result in opaque supply chains, data tampering, fraud, and limited empowerment of smallholder farmers. This systematic review identifies and analyzes 12 representative studies published between 2017 and 2025, selected via a structured search across IEEE Xplore, Scopus, and Google Scholar using a defined inclusion and exclusion protocol. Studies are examined with particular emphasis on supply-chain traceability, IoT-enabled smart farming, parametric crop insurance, direct farmer-to-buyer marketplaces, and secure farm-data management. Most implementations leverage Ethereum smart contracts or Hyperledger Fabric, integrate IoT sensors for real-time monitoring, and employ IPFS for off-chain storage of large files such as sensor readings and images. Key benefits include immutable records that prevent tampering, end-to-end traceability for rapid identification of contaminated produce, automatic smart-contract payments, and trust-building without intermediaries. Notable examples are the Walmart-IBM blockchain pilot for mango and pork traceability and platforms such as Etherisc and Arbol for parametric crop insurance. However, challenges remain, including high gas fees and slow transaction speeds on public blockchains, high energy consumption, interoperability issues, data privacy concerns, and limited digital infrastructure among smallholders in regions such as India. This review synthesizes findings across four core application areas—data storage, supply-chain tracking, smart-contract automation, and security/trust—and identifies six open research gaps. It concludes that dApps have strong potential to make agriculture more transparent, equitable, and sustainable, provided that scalability, usability, and regulatory barriers are addressed.
The agricultural sector is essential for global food security but continues to face challenges in supply chain management, including lack of transparency, traceability, and data integrity. This study proposes AgroChain, a blockchain-based framework designed to enhance governance and trust in the Agricultural Supply Chain (ASC). The system is built on the Quorum blockchain platform, an enterprise version of Ethereum, which integrates Zero-Knowledge Proof (ZKP) protocols to ensure data privacy while maintaining secure and transparent transactions. AgroChain introduces a scalable process model that separates the registry of agricultural records from the actual data, enabling efficient data handling. Smart contracts are used to automate key supply chain operations such as record creation, validation, transfer, and deletion, allowing end-to-end traceability from farm to consumer. The framework also incorporates rolebased access control for stakeholders including farmers, distributors, retailers, and consumers. Experimental results indicate that AgroChain improves transparency, accountability, and interoperability, demonstrating the potential of blockchain technology to transform agricultural supply chain governance.
G. Naveen Kumar, B. Vaishnavi, Ch. Gayathri Bharghavi, K. Shveni
The Smart Contract Farming System is a digitalized platform created to connect farmers and buyers more reliably and transparently. Its main goal is to reduce the gap between both buyers and farmers by using secure digitized agreements that clearly define terms and conditions, which helps to build trust and ensures that transactions are fair and well-structured. The platform is developed using Vite and React, through which users can easily register, explore crop listings, view contracts, and interact in real time. On the backend, Node.js and Express.js handle the core application logic, including API services, authentication, contract processing, and transaction management. All data is securely stored and managed using MongoDB, ensuring consistency and reliability. One of the key features of the system is the Price Prediction Module, which works before a contract is finalized. This module uses agricultural datasets collected from IEEE research publications. With the help of Python-based machine learning models, the system predicts crop prices by analyzing historical data, seasonal trends, and market needs and supply. It helps farmers and buyers in making informed decisions and agreeing on fair prices. Once the price is decided, digital contracts are created and accepted by both parties. This system also includes crops based on agricultural seasons such as Kharif, Rabi, and Zaid, which helps in better planning and avoids mismatches between supply and demand. In addition to contract management, the platform involves features like dispute resolution tools and analytical dashboards, which make the overall process more efficient and transparent. Payments are secured through trusted methods such as UPI and escrow systems, ensuring safe and reliable transactions. The feature that makes this application more effective is the Crop Insurance module, which allows farmers to enroll in government-supported insurance schemes. This protects them from unexpected risks like floods, droughts, or pest attacks. Overall, the system is designed to be scalable, efficient, and user-friendly. It strengthens farmers by giving them assured market access while helping buyers get a consistent and trustworthy supply of crops.
International Journal of Technology, Leadership and Sciences
Food and agriculture supply chain transparency is growing in importance for both consumers and states. The fast expansion of blockchain technology's use is being propelled by its inherent trustworthiness and immutability. This technology can offer safe traceability for the management of the agri-food chain, prevent food fraud, and provide information like a food product's provenance. It is far more difficult than in other businesses to create smart contracts that are suitable for certain use cases. Although many agri-food chain management systems based on smart contracts and blockchain have been developed, they are all quite ad hoc and not easily adaptable to different products or production processes. A new method for quickly adapting and developing universal smart contracts for the agri-food business based on Ethereum is presented in this research. We can automate the process and reuse modules and code using this strategy, which shortens development times without sacrificing dependability and safety. In order to set up a semi-automatic system, we want to start with the production process and build the smart contracts that control the system and the user interfaces that automatically connect with them. To further illustrate how our method works, we provide a case research on honey production. The primary goal of future studies will be to find ways to apply the method to different types of supply chains. Even though Ethereum is now in use, our technology can be simply adapted to other blockchain systems.
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.