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Recognizing different kinds of vegetables and fruits is a difficult task in supermarkets, since the cashier must point table 5 out the categories of a particular fruit to determine its price. The use of barcodes has mostly ended this problem for packaged products but given that most consumers want to pick their products, they cannot be prepackaged, and thus must be weighed. A solution is issuing codes for every fruit, but the memorization is problematic leading to pricing errors. Another solution is to issue the cashier an inventory with pictures and codes, however, flipping over the booklet is time consuming [1].
Some alternatives were proposed to address the problem. VeggieVision was the first supermarket produce recognition system consisting of an integrated scale and image system with a user-friendly interface [2]. Hong et al. [3] employed morphological examination to separate walnuts and hazelnuts into three groups. Baltazar et al. [4] first applied data fusion to nondestructive image of fresh intact tomatoes, followed by a three-class Bayesian classifier. Pennington et al. [5] used a clustering algorithm for classification of fruits and vegetables. Pholpho et al. [6] used visible spectroscopy for classification of non-bruised and bruised longan fruits, and combined this with principal component analysis (PCA), Partial Least Square Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA) to develop classification models.
The aforementioned techniques may have one or several of the following shortcomings: (1) they need extra sensors such as a gas sensor, invisible light sensor, and weight sensor. (2) The classifier is not suitable to all fruits, viz., it can only recognize the varieties of the same category. (3) The recognition systems are not robust GSK-3 because different fruit images may have similar or identical color and shape features [7].Support Vector Machines (SVMs) are state-of-the-art classification methods based on machine learning theory [8]. Compared with other methods such as artificial neural networks, http://www.selleckchem.com/products/PF-2341066.html decision trees, and Bayesian networks, SVMs have significant advantages because of their high accuracy, elegant mathematical tractability, and direct geometric interpretation. Besides, they do not need a large number of training samples to avoid overfitting [9].In this paper, we chose an image recognition method which only needs a digital camera.