Current Status and Development Direction of AI Utilization in the Food Tech Industry Focusing on Deep Learning Technology

This study presents a systematic review of the application and development of artificial intelligence (AI), particularly deep learning technologies, in the food technology industry. The rapid evolution of AI is transforming food production, processing, distribution, and consumption. Following the PRISMA guidelines, this study identified and analyzed 13 peer-reviewed publications (including both journal articles and conference proceedings) published between 2015 and 2024. These studies were selected based on criteria including relevance, empirical rigor, technical contribution, and publication quality. The findings reveal three stages of deep learning advancement in food tech: the foundational stage focusing on food image recognition, the development stage emphasizing accuracy and application diversity, and the integration stage in which AI technologies are applied in real industrial contexts. Furthermore, AI-driven innovations such as robotic services, personalized nutrition recommendations, and intelligent delivery systems are reshaping the consumer experience and operational efficiency. Despite these advancements, technical limitations such as data quality, real-time processing, and cultural adaptability remain critical challenges. This review provides meaningful academic and practical insights into the sustainable digital transformation of the food tech sector. It also highlights the importance of ethical considerations, standardized data practices, and cross-disciplinary talent development to ensure the effective and responsible use of AI in the industry.