Technological Tools Based on Artificial Intelligence in the Sugar Industry: A Bibliometric Analysis and Future Perspectives for Energy Efficiency

Abstract

Introduction: The application of Artificial Intelligence –AI– in industrial sugar production, particularly in sensor data and systems management, is rapidly evolving towards real-time monitoring programs that offer valuable recommendations and decision-making support within the sugar industry. Methodology: This comprehensive bibliometric analysis of 125 Scopus-indexed articles highlights significant trends in the field, including surges in article production during 2017, 2018, 2021, and 2022, accounting for 34% of total publications. Results: Scientific production in this domain grew by 3.93% from 1969 to 2023. Most research (81%) originated from key countries, including Australia, Brazil, India, China, the Philippines, the United States, and France. Prominent journals played a pivotal role, representing 19% of publications. Noteworthy authors include Attard, Everingham, Meng, and Sexton, with four published articles each. Remarkably, 88% of researchers in this field are transitory. This study underscores dynamic growth in artificial intelligence applications in sugar production, emphasizing sustainability in data and systems management. Conclusions: The effective integration of these technologies holds the potential to enhance sustainability practices, optimizing efficiency and quality throughout the sugar production supply chain, thereby contributing to the attainment of Sustainable Development Goal 9. The utilization of artificial intelligence to optimize industrial sugar production represents technological innovation capable of improving the efficiency and infrastructure of the sugar industry, consequently fostering global sustainable development.