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شناسایی و تعیین موقعیت مکانی ناخالصیهای نخود با استفاده کلاسبندهای SVM و KNN | ||
| مهندسی بیوسیستم ایران | ||
| دوره 57، شماره 1، اردیبهشت 1405، صفحه 1-15 اصل مقاله (1.49 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2025.403138.665619 | ||
| نویسندگان | ||
| حسین باقرپور* ؛ سیاوش شامحمدی | ||
| گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران | ||
| چکیده | ||
| در زمان برداشت نخود، انواع مختلفی از ناخالصیها در محصول وجود دارد که لازم است پیش از عرضه به بازار، شناسایی و جداسازی شوند. اگرچه بخش زیادی از این ناخالصیهای براحتی قابل حذف هستند، اما جداسازی مواردی مانند سنگریزههای هماندازه نخود یا نخودهای نارس و بدرنگ با روشهای مرسوم امکانپذیر نیست. هدف این پژوهش، تشخیص نوع و تعیین موقعیت ناخالصیهای مختلف نخود با استفاده از دو مدل هوشمند ماشینبردار پشتیبان (SVM) و K نزدیکترین همسایه (KNN) است. برای این منظور، 400 تصویر RGB تهیه شد که هر کدام از تصویرها شامل شش کلاس نخود سالم، سبز، سیاه، رنگی، سنگ و لپه بودند. برای شناسایی نوع کلاس هر کدام از اشیای موجود در تصویر و استخراج ویژگیها، بعد از تعیین موقعیت مکانی، هر یک از 6 کلاس از تصاویر اصلی جدا گردیدند و به صورت مجزا در 6 دسته مختلف طبقهبندی شدند. با این عملیات، در مجموع کل تعداد تصاویر اشیا به 3840 رسید. ویژگیهایی شامل میانگین، میانه، واریانس، چولگی، هیستوگرام، آنتروپی و نیز ویژگیهای بافتی حاصل از ماتریس هموقوع سطح خاکستری شامل کنتراست، همبستگی، انرژی و همگنی استخراج شد. در مدل SVM، تابع RBF بهترین عملکرد را در مقایسه با توابع دیگر نشان داد. در مدل KNN نیز بهترین نتایج با 13k=، معیار فاصله City Block و وزندهی (c+D²)/1 با 1c= حاصل شد. تعیین موقعیت مکانی اشیا بر اساس مختصات مرکز آنها در محیط MATLAB انجام گرفت. بر اساس نتایج، بیشترین دقت مدلهای SVM و KNN در رزولوشن 250×250 به ترتیب برابر با 09/98 و 88/90 درصد بهدست آمد. | ||
| کلیدواژهها | ||
| درجه بندی؛ پردازش تصویر؛ حبوبات؛ ناخالصی نخود | ||
| عنوان مقاله [English] | ||
| Identification and Localization of Chickpea Impurities Using SVM and KNN Classifiers | ||
| نویسندگان [English] | ||
| hossein bagherpour؛ Siavash Shamohammadi | ||
| Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran | ||
| چکیده [English] | ||
| 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. | ||
| کلیدواژهها [English] | ||
| Classification, Image processing, Beans, Pea impurities | ||
| مراجع | ||
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