Artificial Intelligence in Food Manufacturing: A Review of Current Work and Future Opportunities

Abstract

The incorporation of Artificial Intelligence (AI) could deliver a new era in food manufacturing, marked by increased operational efficiencies, higher product quality, and better safety standards. This review offers an in-depth examination of the field's evolution, outlines the leading AI methodologies, and investigates their applications in food manufacturing. This review begins with an introduction to AI and its historical context before classifying the main AI methods used in food manufacturing as machine learning, computer vision, robotics, and natural language processing. Machine learning has emerged as an AI application in many areas of food manufacturing due to its ability to learn from data and make predictions. Computer vision is a popular form of AI and plays an important role in visual inspections, ensuring product consistency and detecting defects. Robotics, in conjunction with AI, has automated a wide range of labour-intensive tasks, from packaging to palletizing, resulting in significant improvements in operational efficiency. Natural language processing has found applications in customer service and compliance, allowing for more efficient interactions and regulatory compliance. AI applications in food manufacturing are numerous and diverse and key areas such as ingredient sorting, quality assessment, process optimization, and supply chain management are highlighted in this review. Finally, we present issues that the industry is encountering in the implementation of AI, as well as a research agenda based on the findings. In-depth analysis provided in this review including the field's evolution, main AI methods used, and their applications in food manufacturing, can provide valuable insights for researchers, practitioners, and decisionmakers in applications of AI in food manufacturing.