Automatic Classification of Fruits and Vegetables: A Texture-Based Approach

Fruits and Vegetables are very important food product for the daily life of the humans. Classification of fruits and vegetable is needed for every aspect of the agricultural industry. It is quite challenging to automatically classify fruits and vegetables from digital images. The task of automatic classification becomes more difficult when the image is captured from a different viewing angle. This paper proposes a complete texture-based approach for addressing the effect of viewing angle change to classify fruits and vegetables automatically. At first, a grayscale image is generated from the input color image. The grayscale version of the input image is used to extract multiple threshold values using the multilevel Otsu thresholding technique. Those threshold values are used to generate a set of binary images. The binary images pass through a border extraction process to generate the border image of every binary image. Finally, the border image is processed to calculate the fractal dimension. In parallel flow, the same grayscale image is processed to compute gray-level co-occurrence matrix based features. The fractal dimension and gray-level co-occurrence matrix based features are combined to make a feature vector for classifying the fruit and vegetable classes. Images are collected by covering the entire range of 0\(^\circ \)–360\(^\circ \) angle for each class in our dataset. In total, 1656 images of 23 classes of fruits and vegetables are used for experimentation. The maximum accuracy of the system is 98.33% with Naive Bayes classifier.

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