Olive-Fruit Variety Classification by Means of Image Processing and Convolutional Neural Networks

Abstract

The automation of classifcation and grading of horticultural products attending to different features comprises a major challenge in food industry. Thus, focused on the olive sector, which boasts of a huge range of cultivars, it is proposed a methodology for olive-fruit variety classifcation, approaching it as an image classifcation problem. To that purpose, 2,800 fruits belonging to seven different olive varieties were photographed. After processing these initial captures by means of image processing techniques, the resulting set of images of individual fruits were used to train, and continuedly to externally validate, the implementations of six different Convolutional Neural Networks architectures. This, in order to compute the classifers with which perform the variety categorization of the fruits. Remarkable hit rates were obtained after testing the classifers on the corresponding external validation sets. Thus, it was yielded a top accuracy of 95.91% when using the Inception-ResnetV2 architecture. The results suggest that the proposed methodology, once integrated into industrial conveyor belts, promises to be an advanced solution to postharvest olive-fruit processing and classifcation.