Corn, one of the agricultural products widely grown in the world, is an important nutrient for both humans and animals. Within the scope of this study, four corn cultivars (BT6470, Calipos, Es Armandi, and Hiva) licensed and produced by BIOTEK, were classified based on morphological, shape, and color features extracted from high-resolution RGB images. A dataset consisting of 14,469 individual seed images was constructed to support this classification task. A total of 106 features were extracted from each image and subsequently classified using three machine learning algorithms: Neural Network, Logistic Regression, and Random Forest. In the second stage, the Gray Wolf Optimizer (GWO) algorithm was applied to select and reduce the features to 44. In the third stage, 57 features were selected from the initial set using the Particle Swarm Optimization (PSO) algorithm. As a result, when the classification performances of all three stages were compared, it was found that the Neural Network was the most successful method with accuracy rates of 95.31 %, 95.09 % and 94.72 %, respectively. The results of the study show that the reduced number of features significantly reduces training and testing times. It is seen that the success performance does not change significantly in the classification made by reducing the optimization algorithms of the attribute numbers, and the calculation costs decrease.