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.