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Computer Vision for Quality Control in Latin American Food Industry, A Case Study

Today quality and safety are major drivers for the food industry. In this context, computer vision technologies might prove highly relevant, providing objective measurements of relevant visual attributes related to food quality and safety, such as, the shape or color of a given good. As a major producer of food products, this type of technologies are highly relevant for Latin America. In this paper we analyze and share our experience developing computer vision technologies for the food industry in a Latin American country. In particular, as a case study, we describe VisionQ-Lab, an application of computer vision technologies to classify seeds, beans, and grains. To demonstrate the capabilities of VisionQ-Lab, we present the results of testing its operation using real data corresponding to different types of rice grains and lentils provided by a food company. The results of our test indicate an average accuracy in the classification close to 95%.