Title: A new method for automatic date fruit classification

Authors: Oussama Aiadi; Mohammed Lamine Kherfi

Addresses: LAGE Laboratory, Université Kasdi Marbah, Street of Ghardia, Ouargla 30 000, Algeria ' LAMIA Laboratory, Université du Québec à Trois-Rivières, 3351, boul. des Forges, C.P. 500, Trois-Rivières, Canada; Université Kasdi Marbah, Ouargla 30 000, Algeria

Abstract: Date fruit classification by human is tedious, slow and requires several workers. In this paper, we propose a method for automatic classification of dates. Because dates of the same variety may considerably vary in terms of hardness, maturity level and shape, we represent each variety with a Gaussian mixture model (GMM). Calinski-Harabasz index has been adopted to estimate the optimal number of components for each GMM. Furthermore, the normality of samples belonging to each component is checked using Mardia's multivariate tests. Our method is able to accurately classify dates in spite of the large variation within some varieties and the small variation between some varieties. Moreover, it doesn't require any human intervention. To validate our method and as, to our knowledge, no date benchmark is publicly available; we introduce a new benchmark of 5,000 images from ten varieties. Experimental results demonstrate the effectiveness and the strength of our method.

Keywords: date fruit; date classification; Gaussian mixture model; GMM; date benchmark.

DOI: 10.1504/IJCVR.2017.087751

International Journal of Computational Vision and Robotics, 2017 Vol.7 No.6, pp.692 - 711

Received: 22 Apr 2016
Accepted: 12 Jul 2016

Published online: 24 Jul 2017 *

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