Zhu Lili, Spachos Petros, Pensini Erica, Plataniotis Konstantinos N
School of Engineering, University of Guelph, Guelph, ON, N1G 2W1, Canada.
Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, M5S 3G4, Canada.
Curr Res Food Sci. 2021 Apr 15;4:233-249. doi: 10.1016/j.crfs.2021.03.009. eCollection 2021.
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.
食品质量与安全是整个社会的重要问题，因为它是人类健康、社会发展与稳定的基础。确保食品质量与安全是一个复杂的过程，必须考虑食品加工的各个阶段，从种植、收获和储存到制备和消费。然而，这些过程往往劳动密集。如今，机器视觉的发展能够极大地帮助研究人员和行业提高食品加工效率。因此，机器视觉已广泛应用于食品加工的各个方面。同时，图像处理是机器视觉的重要组成部分。图像处理可以利用机器学习和深度学习模型来有效识别食品的类型和质量。随后，机器视觉系统中的后续设计可以解决诸如食品分级、检测缺陷点或异物位置以及去除杂质等任务。在本文中，我们概述了可应用于食品加工领域的传统机器学习和深度学习方法以及机器视觉技术。我们介绍了当前的方法和挑战以及未来趋势。
