A concise review on food quality assessment using digital image processing

Background: Recent advances in signal processing technology and computational power have increased the attention towards computer vision-based techniques in diverse applications such as agriculture, food processing, biomedical, and military. Especially in agricultural and food processing, computer vision can replace most of the manual methods for screening of seed, grain and food quality. Scope and approach: The objective of present study is to review the recent advancements in computer vision techniques for predicting quality of various raw materials and food products. This review paper is focused on the quality determination of grains, vegetables, fruits, beverages, meat, sea food and edible oils using Digital Image Processing (DIP). Several studies have reported the successful applications of DIP techniques for feature extraction, classification and quality prediction of foods. DIP algorithms are used to extract the significant features from images which are further used as input for machine learning (ML) algorithms to classify them based on different criteria. These feature extraction methods have been improved by Deep Learning (DL) algorithms. Features can be automatically extracted by DL algorithms resulting in higher accuracy. DL algorithms require huge data management and computational resources which can be a major limitation. Key findings and conclusion: A significant literature is available for quality estimation of food products by using computer vision algorithms, but they lack commercial exploitation. Android based applications have not yet been developed for this specific purpose. User friendly, low cost and portable devices equipped for quality estimation would be helpful for rapid quality measurement of food products in real time.