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.
Khoya (khoa or mawa) is a traditional dairy product, prepared by heating and concentrating milk, which is widely used in preparation of indigenous milk sweets. But, challenges such as process variability, quality deterioration, microbial contamination, adulteration, limited shelf life and inefficient supply chain management hinder its production and distribution. New solutions to these challenges are available across the khoya value chain due to recent advancements in artificial intelligence (AI) and Industry 4.0 technologies. This review highlights the applications of AI in khoya processing, packaging, transportation, distribution and quality management. The role of machine learning, deep learning, computer vision, Internet of Things (IoT), digital twins, smart sensors, and blockchain in process optimization, automated quality inspection, adulteration detection, shelf-life prediction, intelligent packaging, cold-chain monitoring, logistics optimization and demand forecasting is explored. We also review AI-enabled analytical tools for rapid and non-destructive quality assessment, such as hyperspectral imaging, electronic nose, and electronic tongue. The review also discusses the contribution of AI to improving food safety, traceability, sustainability and operational efficiency, as well as to reducing post-harvest losses and environmental impacts. Finally, the paper discusses the existing challenges, future research directions, and prospects of AI-enabled smart dairy manufacturing. The review finds that AI can play a significant role in improving the quality, safety, efficiency, and sustainability of the khoya industry and helping its transition to intelligent and data-driven dairy processing.