Applications and Prospects of Machine Learning in Grain Processing and Quality Inspection

Abstract

Food security is a core issue for global sustainable development, and the rapid advancement of machine learning technologies is driving transformative changes in grain processing and quality inspection. This review systematically elaborates on the fundamental theoretical framework of machine learning in grain processing, encompassing algorithm evolution, intelligent model construction, and data acquisition and processing systems, and conducts an in-depth analysis of grain quality inspection involving method comparison, image recognition and component analysis. We further discuss the core driving forces for the technological progress of grain processing, including the intelligent control of automated equipment, efficiency improvement strategies and real-time monitoring technologies, while critically evaluating the prevailing challenges in standardization, detection accuracy and data security, combined with a retrospective analysis of its historical development and current industrial application status. Finally, we outline the future development trends, emerging technological potential, and relevant policy and regulatory directions of this field. Distinct from existing reviews, this work integrates multidisciplinary research findings to systematically demonstrate how machine learning optimizes grain processing workflows, improves detection accuracy and safeguards food safety, thereby providing theoretical foundations and practical pathways for constructing an efficient, intelligent and sustainable grain industry system.