MATLAB-Based Fruit Recognition Using Image Processing Techniques

This MATLAB-based project focuses on utilizing image recognition technology for fruit identification. The implementation employs various algorithms and techniques to process and analyze fruit images for accurate classification. Key technical components include feature extraction methodologies based on color histograms (using rgb2hsv and color thresholding), shape descriptors (regionprops for area, perimeter, and eccentricity calculations), and texture analysis (gray-level co-occurrence matrix features via graycomatrix and graycoprops). The classification system integrates machine learning algorithms such as Support Vector Machines (fitcsvm) or k-Nearest Neighbors (fitcknn) for pattern recognition. This project provides practical insights into applying image processing techniques to real-world problems while demonstrating the integration of fruit characteristics with multidisciplinary knowledge from biology, agriculture, and food science. The code structure typically follows: image preprocessing (imread, imresize), feature extraction, classifier training (trainClassifier), and validation (predict) phases, offering comprehensive learning opportunities in computer vision applications.