A Recognition and Classification of Fruit Images Using Texture Feature Extraction and Machine Learning Algorithms

Fruits classification is demanded in some fields, such as industrial agriculture. Automatic fruit classification from their digital image plays a vital role in those fields. The classification encounters several challenges due to capturing fruits’ images from different viewing angle, rotation, and illumination pose. In this paper a framework for recognition and classification of fruits from their images have been proposed depending on texture features, the proposed system rely on three phases; firstly, pre-processing, as images need to be resized, filtered, color convert,  and threshold in order to create a fruit mask which is used for fruit’s region of interest segmentation; followed by two methods for texture features extraction, first method utilize Local Binary Pattern (LBP), while the second method uses Principal Component Analysis (PCA) to generate features vector for each fruit image. Classification is the last phase; two supervised machine learning algorithms; K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) are utilized to identity and recognize the fruits images classes. Both methods are tested using 1200 fruits images, from 12 classes acquired from Fruits-360 database. The results show that combining LBP with K-NN, and SVM yields the best accuracy up to 100% and 89.44% respectively, while the accuracy of applying PCA with K-NN and SVM reached to 86.38 % and 85.83% respectively.