Pumpkin seeds are frequently consumed as confection worldwide because of their adequate amount of protein, fat, carbohydrate, and mineral contents. This study was carried out on the two most important and quality types of pumpkin seeds, “Ürgüp Sivrisi” and “Çerçevelik”, generally grown in Ürgüp and Karacaören regions in Turkey. However, morphological measurements of 2500 pumpkin seeds of both varieties were made possible by using the gray and binary forms of threshold techniques. Considering morphological features, all the data were modeled with five different machine learning methods: Logistic Regression (LR), Multilayer Perceptrons (MLP), Support Vector Machine (SVM) and Random Forest (RF), and k-Nearest Neighbor (k-NN), which further determined the most successful method for classifying pumpkin seed varieties. However, the performances of the models were determined with the help of the 10 k-fold cross-validation method. The accuracy rates of the classifiers were obtained as LR 87.92 percent, MLP 88.52 percent, SVM 88.64 percent, RF 87.56 percent, and k-NN 87.64 percent.

南瓜籽由于含有充足的蛋白质、脂肪、碳水化合物和矿物质，因此在世界范围内经常作为糖果食用。本研究针对两种最重要和最优质的南瓜种子类型“Ürgüp Sivrisi”和“Çerçevelik”进行，它们通常生长在土耳其的 Ürgüp 和 Karacaören 地区。然而，通过使用灰色和二元形式的阈值技术，可以对这两个品种的 2500 颗南瓜种子进行形态测量。考虑到形态特征，所有数据都使用五种不同的机器学习方法建模：逻辑回归 (LR)、多层感知器 (MLP)、支持向量机 (SVM) 和随机森林 (RF)，以及 k-最近邻 (k-NN) )，这进一步确定了最成功的南瓜种子品种分类方法。然而，模型的性能是在 10 k 折交叉验证方法的帮助下确定的。分类器的准确率分别为 LR 87.92%、MLP 88.52%、SVM 88.64%、RF 87.56% 和 k-NN 87.64%。