Feature Extraction of Fruit Images

In this documentation, my graduation project focuses on feature extraction from fruit images. The implementation is straightforward - by executing the "apple.m" MATLAB script, users can effortlessly perform the entire feature extraction process. This practical feature extraction method enables rapid and accurate identification and classification of fruit images. The algorithm employs computer vision techniques including color histogram analysis, texture feature extraction using GLCM (Gray-Level Co-occurrence Matrix), and shape characterization through boundary detection. Beyond apples, the method can be extended to extract features from various other fruits such as bananas, oranges, pears, and more through simple parameter modifications in the configuration file. The code architecture is designed with modularity in mind, featuring key functions like preProcessing() for image enhancement, colorFeatureExtraction() for RGB analysis, and textureAnalysis() for surface pattern recognition. The implementation is intuitive and accessible, suitable for both professionals and beginners in image processing. I hope this graduation project outcome can provide valuable references and inspiration for research and practical applications in related fields, particularly in agricultural technology and computer vision applications.