The quality and safety of food is an important issue to the whole society,
since it is at the basis of human health, social development and stability.
Ensuring food quality and safety is a complex process, and all stages of food
processing must be considered, from cultivating, harvesting and storage to
preparation and consumption. However, these processes are often
labour-intensive. Nowadays, the development of machine vision can greatly
assist researchers and industries in improving the efficiency of food
processing. As a result, machine vision has been widely used in all aspects of
food processing. At the same time, image processing is an important component
of machine vision. Image processing can take advantage of machine learning and
deep learning models to effectively identify the type and quality of food.
Subsequently, follow-up design in the machine vision system can address tasks
such as food grading, detecting locations of defective spots or foreign
objects, and removing impurities. In this paper, we provide an overview on the
traditional machine learning and deep learning methods, as well as the machine
vision techniques that can be applied to the field of food processing. We
present the current approaches and challenges, and the future trends.
Lili Zhu, Petros Spachos, Erica Pensini, Konstantinos Plataniotis