A practical framework for automatic food products classification using computer vision and inductive characterization

With the increasingly international regulatory demands for food products import and export, as well as with the increased awareness and sophistication of consumers, the food industry needs accurate, fast and efficient quality inspection means. Each producer seeks to ensure that their products satisfy all consumer׳s expectations and that the appropriate quality level of each product is offered and sold to each different socio-economic consumer group. This paper presents a framework that uses computer vision and inductive characterization with a reduced set of features, along with three cases where this framework has been successfully applied to improve the quality inspection process. Three different basic food products are studied: Hass Avocado, Manila Mango and Corn Tortillas. These products are very important in economical terms for the sheer volume of their production and marketing. Each product has particular characteristics that involve different ways of handling the quality inspection process, but this framework allows addressing common key points that allow automatizing this process. Experimental results of each case shows that the proposed technique is competitive with existing systems and has significantly lower costs in terms of the number of features required for classification.