Correct grading of corn for food production raises the standard of products offered to consumers and maintains product quality. Classification ensures optimal storage and processing conditions. As a result, losses are minimized, costs are reduced, and agriculture becomes more sustainable. When dealing with huge data, classification needs to be done quickly and accurately. A faster way of achieving the same classification success was explored in this study. Deep learning models ResCNN, DAG-Net, and ResNet-18 were used to classify three corn varieties named Chulpi Cancha, Indurata, and Rugosa. With 1050 corn images, the classification process was carried out. A total of three datasets were obtained using Canny edge detection algorithm (CEDA), Sobel edge detection algorithm (SEDA), and normal color images (CI). Based on experimental studies with CI, the accuracy values of 0.9952, 1, 0.9952; 0.9933, 1, 0.9933; and 0.9952, 1, 0.9952 were obtained for Chulpi Cancha, Indurata, Rugosa corn varieties using ResCNN, DAG-Net, and ResNet-18 deep learning models, respectively. With the images generated by CEDA, the accuracy values for Chulpi Cancha, Indurata, and Rugosa corn varieties were 0.9904, 1, 0.9904; 0.9952, 0.9990, 0.9961; and 0.9952, 1, 0.9952, respectively. Using ResCNN, DAG-Net, and ResNet-18 deep learning models, accuracy values were obtained. Based on the images obtained through SEDA, the accuracy values for Chulpi Cancha, Indurata, and Rugosa corn varieties were 0.9933, 1, 0.9933; 0.9952, 1, 0.9952; and 0.9952, 1, 0.9952 using ResCNN, DAG-Net, and ResNet-18 deep learning models, respectively. ResCNN, DAG-Net, and ResNet-18 models trained faster than CI.