Application of deep learning in food

AbstractWith a significant number of successful examples in image processing, voice recognition, object identification, and other areas, deep learning has shown to be an advanced technique for big data analysis. It's also being used in food science and engineering recently. This is the first review in the food realm that we are aware of. We gave a short introduction to deep learning in this article, as well as comprehensive descriptions of the structure of several common deep neural network designs and training methods. We looked at hundreds of papers that utilized deep learning as a data analysis technique to address issues and difficulties in the food domain, such as food identification, calorie estimate, fruit, vegetable, meat, and aquatic product quality detection, food supply chain, and food contamination. Each study looked at the particular issues, datasets, preprocessing techniques, networks and frameworks utilized, performance obtained, and comparisons with other popular solutions. We also looked at the possibility of using deep learning as an enhanced data mining technique in food sensory and consumption studies. Deep learning surpasses other techniques such as manual feature extractors, traditional machine learning algorithms, and deep learning as a potential tool in food quality and safety inspection, according to the results of our study. Deep learning's promising achievements in classification and regression issues will spur further study into using deep learning to the area of food in the future.