Abstract The main target of the current study is to review the latest developments in accurate, reliable, and low-cost non-contact or remote techniques, including the usage of artificial intelligence (AI)-based methods, image processing (IP) system, and sensor technology for quality assessment in the food industry (FI). The IP systems and AI can be used for various purposes, such as classifying products based on size and shape, detecting product defects, the presence of microbes, and grading food quality. The sensor technology is now highly developed to assess food products' quality and safety due to the dramatic growth of nanotechnology and biotechnology. Also, in this paper, it was tried to examine the role of robots in the FI and discusses the advantages and disadvantages of using them in the FI.

Abstract International food trade necessitates the existence of effective anti-tampering systems to protect against food toxins, thus making this kind of contamination difficult to get around. Due to a short growing season, fresh fruits and vegetables are easily damaged by spoilage and can lose much of their value. Being transported and stored is dangerous. Due to these risks, food prices will continue to rise for fresh produce. They are in such short supply that the fresh products will have a dramatic and urgent need for the supply chain. The journey to food and nutrition protection starts with a thorough examination of the problems that must be addressed in order to establish appropriate solutions. As a result, decision-making is defined as the process of selecting the best option from a set of options. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work well among various multi-criteria decision-making models. Based on the TOPSIS method, this study focuses on the risk level of a food production enterprise.

Abstract With numerous promising cases in image processing, voice recognition, target detection, and other fields, deep learning (DL) have proven to be an advanced tool for big data analysis. It's been used in food science and engineering recently as well. This is the first food-related study that we are aware of. We gave a brief overview of DL in this paper, as well as comprehensive descriptions of the structure of some common deep neural network (DNN) architectures and training approaches. We looked at hundreds of publications that used DL as a data processing method to address problems and issues in the food domain, such as food identification, calorie estimating, fruit, potato, meat, and aquatic commodity quality detection, food supply chain, and food pollution. Each study looked at the particular challenges, datasets, preprocessing techniques, networks and systems used, the efficiency achieved, and comparisons with other common solutions. We examined the degree to which big data is being used in the food safety domain and found some positive developments in this article. According to our study results, DL outperforms other approaches such as manual attribute extractors, traditional machine learning algorithms, and DL as a promising technique in food quality and safety inspection.