Learning techniques used in computer vision for food quality evaluation: a review

Abstract

Learning techniques have been applied increasingly for food quality evaluation using computer vision in recent years. This paper reviews recent advances in learning techniques for food quality evaluation using computer vision, which include artificial neural network, statistical learning, fuzzy logic, genetic algorithm, and decision tree. Artificial neural network (ANN) and statistical learning (SL) remain the primary learning methods in the field of computer vision for food quality evaluation. Among the applications of learning algorithms in computer vision for food quality evaluation, most of them are for classification and prediction, however, there are also some for image segmentation and feature selection. In this paper, the promise of learning techniques for food quality evaluation using computer vision is demonstrated, and some issues which need to be resolved or investigated further to expedite the application of learning algorithms are also discussed.