Fruit Classification System Using Multiclass Support Vector Machine Classifier

Fruit classification has always been a challenging task as it involves sorting, grading, and analyzing fruit quality specifically for import, consumption purposes and for workers at the Point of Sale (POS) in supermarkets. The proposed work has used computer vision and support vector machine (SVM), for classification. Among the fruit images collected, 505 fruit images are used in training and 150 fruit images are used in testing. The work is carried out in various stages. First, the input image is resized to 256×256 resolutions. Later, pre-processing is performed using Gaussian filter to enhance the image quality by reducing the noise. Feature space is created by extracting the colour, texture and shape features. Then, principal component analysis (PCA) is applied to reduce the dimensions of the feature space to overcome the curse of dimensionality. Further, the support vector machine (SVM) classifier is used for training the data. Total of 655 images distributed across 18 categories of fruits are maintained that achieves 87.06% accuracy.

I. Introduction

Fruit classification involves human labour in turn leading to time consuming process in fruit store markets and it is vital specifically for the clerk in understanding the classes of a specific type of fruit. In order to decide or choose it's expense. The usage of scanner labels has illuminated this issue particularly on the bundled products; however most buyers hand pick the things without anyone else. A few natural products and fruits can't be bundled utilizing scanner tags and in this way should be weighted. A vague solution can be to give codes for every fruit product, anyway the redundant recognition of institutionalize labels brief bumbles in assessing, Another solution can be to give the clerk a stock of pictures of fruit and its associated codes; however, flipping over the inventory booklet at the point of sale is frustrating to them as well as the customer who has to wait for a longer duration [1].