Deep learning and machine vision for food processing: A survey

Abstract

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.

Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification

“…Hence, subjecting all the models to the voting technique improves the classification performance. It is also worth noting that deep learning and other machine learning approaches have recently been successful in various food-related spectroscopic data analysis ( Zhu et al, 2021 ; Zheng et al, 2014 ; Feng et al, 2021 ; Wang et al, 2021 ; Liang et al, 2020 ; Yan et al, 2021 ; He et al, 2021 ); thus, we expect that the considered machine learning and deep learning methods will work-well in the classification of various adulterant honey samples using NMR spectroscopy in the proposed context as well. As schematically shown in Fig.…”

Potato Surface Defect Detection Based on Deep Transfer Learning

“…Compared to the feature-based conventional learning algorithms, deep learning can learn features automatically during training and demonstrate superior performance in a wide spectrum of computer vision tasks, including image classification and object detection [21,22]. Recent years have witnessed the development of deep learning-based optical sorters [23,24], which have been proven to be more efficient and effective than traditional machine vision-based systems [25].…”

One-Dimensional Convolutional Neural Networks for Hyperspectral Analysis of Nitrogen in Plant Leaves

“…The uses of CNN have grown so rapidly that, in a short time, they have revolutionized many areas of computer vision, such as human action detection, object detection, face detection and tracking. In this regard, Zhu et al [13] conducted a review of traditional machine learning and deep learning methods, including AlexNet, VGG Net and fully convolutional networks (FCN). According to their declaration, despite the success of the machine vision methods, they will have low accuracy if the background is too noisy, or the lighting conditions are poor, since they will not be able to detect small changes in the food.…”