University of Tehran Press Iranian Journal of Biosystem Engineering 2008-4803 57 1 2026 04 21 Identification and Localization of Chickpea Impurities Using SVM and KNN Classifiers Identification and Localization of Chickpea Impurities Using SVM and KNN Classifiers 1 15 106676 10.22059/ijbse.2025.403138.665619 FA Hossein Bagherpour Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran 0000-0002-6877-6281 Siavash Shamohammadi Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran 0009-0000-2916-4285 Journal Article 2025 09 27 During chickpea harvesting, various types of impurities are present in the product, which must be identified and removed before market distribution or use as seed. Although pneumatic and mechanical methods can eliminate a substantial portion of these impurities, conventional techniques are insufficient for separating objects such as small stones of similar size to chickpeas or unripe and discolored grains. The objective of this study was to identify the type and determine the location of different chickpea impurities using two intelligent classifiers: Support Vector Machine (SVM) and k-Nearest Neighbors (KNN). For this purpose, 400 RGB images were acquired, encompassing six classes: healthy, green, black, colored, stones, and split chickpeas. After object segmentation and classification into six groups, the total number of samples reached 3,840. Features extracted included mean, median, variance, skewness, histogram, entropy, and texture descriptors derived from the gray-level co-occurrence matrix (GLCM), such as contrast, correlation, energy, and homogeneity. In the SVM model, the RBF kernel exhibited superior performance compared to other kernels. For KNN, the optimal results were obtained with k = 13, the City Block distance metric, and a weighting scheme of 1/(c + D²) with c = 1. Object localization was performed in MATLAB by determining the coordinates of each object's center. Based on the results, the highest classification accuracy for the SVM and KNN models at a resolution of 250×250 pixels were 98.09% and 90.88%, respectively. During chickpea harvesting, various types of impurities are present in the product, which must be identified and removed before market distribution or use as seed. Although pneumatic and mechanical methods can eliminate a substantial portion of these impurities, conventional techniques are insufficient for separating objects such as small stones of similar size to chickpeas or unripe and discolored grains. The objective of this study was to identify the type and determine the location of different chickpea impurities using two intelligent classifiers: Support Vector Machine (SVM) and k-Nearest Neighbors (KNN). For this purpose, 400 RGB images were acquired, encompassing six classes: healthy, green, black, colored, stones, and split chickpeas. After object segmentation and classification into six groups, the total number of samples reached 3,840. Features extracted included mean, median, variance, skewness, histogram, entropy, and texture descriptors derived from the gray-level co-occurrence matrix (GLCM), such as contrast, correlation, energy, and homogeneity. In the SVM model, the RBF kernel exhibited superior performance compared to other kernels. For KNN, the optimal results were obtained with k = 13, the City Block distance metric, and a weighting scheme of 1/(c + D²) with c = 1. Object localization was performed in MATLAB by determining the coordinates of each object's center. Based on the results, the highest classification accuracy for the SVM and KNN models at a resolution of 250×250 pixels were 98.09% and 90.88%, respectively. https://ijbse.ut.ac.ir/article_106676_d91fd87c915abca0e4b05ee10d7b2077.pdf