Classification of Sun‐Dried Bulk Raisins by Using Statistical Texture Features

Classification of bulk sun-dried raisins as one of the most important export products of Iran poses a significant challenge for producers and buyers due to their visual similarity to impurities and damaged grains. This research provides an expert system to evaluate the classification of sun-dried dark-color bulk raisins with dark color-based impurities by analyzing images of bulk raisins. For this purpose, a machine vision setup was employed to capture 525 images of raisins across various mixture ranges. Subsequently, textural features were extracted using three methods of Gray Level Co-occurrence Matrix (GLCM), Gray-Level Run-length Matrix (GLRM), and Local Binary Pattern (LBP) to comprehensively analyze the textural properties. Various classification models including Decision Tree (DT), Discriminate Analysis (DA), Support Vector Machine (SVM), K-Nearest Neighborhood (KNN), and Artificial Neural Network (ANN) models were applied. DA model achieved the accuracy of 98.61% and 97.92% on the test datasets for all and GLCM features, respectively. In order to optimize feature importance, Maximum Relevance Minimum Redundancy (MRMR) and Chi-Square Test (CST) algorithms were employed, and achieved accuracies of 95.83% and 77.38% for 6-class and 7-class datasets, respectively. Therefore, the results of the proposed approach can be utilized in designing a system for measuring the purity and quality of raisins.