Deep learning based image processing solutions in food engineering: A review Begum Ninja*, Hazarika Manuj Kumar Department of Food Engineering and Technology, Tezpur University, Sonitpur-784 028, Assam, India *Corresponding Author: Ninja Begum, Department of Food Engineering and Technology, Tezpur University, Sonitpur-784 028, Assam, India, Email: ninzasworld@gmail.com
Online Published on 01 October, 2022. Abstract Image based assessment of food quality for wholesomeness, nutritional composition, suitability as raw material for processing, degree of processing, product aesthetics, consumer attractiveness etc., are some of the aspirations for applying machine learning in food technology. The initial contributions made by machine learning in the field of artificial intelligence are now more prominent through the techniques of deep learning. This review presents the contributions of machine learning in obtaining image processing based solutions in food technology and the relative advantages of deep learning over machine learning as the technique for solving complex problems like image recognition and image classification. The deep learning based solutions to the problems of image processing are highlighted as the enablers of disruptions in the design and development of different sorting, grading and dietary assessment tools. Top Keywords Classification, Deep learning, Food, Identification, Machine learning. Top |