Revolutionizing Food Quality With Machine Vision and Machine Learning Techniques

Abstract

Ensuring food quality and safety is a complex and multi-layered issue that must take into account all stages of food processing, from cultivation and harvesting to storage, transportation, and consumption. The application of machine learning in the food industry can significantly improve work efficiency and ensure food quality and safety. In addition to traditional food quality assessment applications, deep learning techniques are also being used for more complex tasks such as detecting food defects, foreign objects, and freshness. This chapter discusses various applications of machine vision technology in conjunction with machine learning in the food sector, such as image recognition, classification, quality control, and the food industry chain. In addition, several challenges related to the cost of obtaining and annotating food datasets are discussed. Furthermore, future research needs are discussed to further investigate how to improve the quality and scope of the datasets, optimise the robustness and interpretability of the different models used in food quality assessment systems.