Almonds classification using supervised learning methods

Abstract

Digital image processing techniques are commonly employed for food classification in an industrial environment. In this paper, we propose the use of supervised learning methods, namely multi-class support vector machines and artificial neural networks to perform classification of different type of almonds. In the process of defining the feature vectors, the proposed method has relied on the principal component analysis to identify the most significant shape and color parameters. The comparative analysis of considered classification algorithms has shown that the higher levels of accuracy in almond classification are attained when support vector machine are used as the basis for classification, rather than artificial neural networks. Moreover, the experimental results have demonstrated that the proposed method exhibits significant levels of robustness and computational efficiency to facilitate the use in the real-time applications. In addition, for the purpose of this paper, a dataset of almond images containing various classes of almonds is formed and made freely available to be used by other researchers in this field.