AI Based Food Quality Recommendation System

Abstract

Deep learning has been evidenced to be a  cutting-edge technology for big data scrutiny with a huge  figure of effective cases in image processing, speech  recognition, object detection, and so on. Lately, it has also  been acquainted with in food science and business. In this  paper, a fleeting overview of deep learning and detailly  labelled the structure of some prevalent constructions of deep  neural networks and the method for training a model is  provided. Various techniques that used deep learning as the  data analysis tool are analyzed to answer the complications  and challenges in food sphere together with quality  detection of fruits & vegetables. The precise difficulties,  the datasets, the pre-processing approaches, the networks  and frameworks used, the performance attained, and the  evaluation with other prevalent explanations of each  research are examined. We also analyzed the potential of  deep learning to be used as a cutting-edge data mining tool  in food sensory and consume explores. The outcome of  our review specifies that deep learning outclasses other  approaches such as physical feature extractors, orthodox  machine learning algorithms, and deep learning as a capable  tool in food quality and safety inspection. The cheering  outcomes in classification and regression problems attained  by deep learning will fascinate more research exertions to  apply deep learning into the arena of food in the  forthcoming. The main aim of this work is to facilitate our  learning and implement that in real life. Food quality and  food security are always issues which are always overlooked.  In modern times, this has morphed into more significant  concerns relating to optimization of on- demand supply  chains and profitability of agri-businesses. But now with the advanced systems and technology, it is possible to resolve  this issue efficiently using the power of AI.