Walnut is an agricultural product with high economic value on a global scale. Walnut species classification is essential for research, conservation, and quality control, yet traditional methods rely heavily on manual identification, which is a time-consuming procedure and subject to human mistakes. With technological developments in the agricultural sector, making use of deep learning algorithms in the classification of products such as fruits, vegetables and grains were becoming increasingly widespread. In this study, it is aimed to automatically classify walnut species using pre-trained deep learning models. Thus, it is aimed at reducing the loss of time, workload and error rates in sorting processes. For this purpose, a dataset consisting of images belonging to Chandler, Kaman1, Fernor, Yalova3 and Maras18 walnut species was created. The dataset consists of 2540 images in total. The images were trained using VGG16, VGG19, ResNet-50, DenseNet-121, and Xception models. The classification was performed using pre-trained deep learning architectures, including VGG16, VGG19, ResNet-50, DenseNet-121, and Xception. Among these models, ResNet-50 delivered the best performance with an accuracy of 97.95% on the original dataset, while the Xception model excelled with 98.54% accuracy when trained with a weighted loss function and 98.27% accuracy with data augmentation. These findings highlight the effectiveness and reliability of ResNet-50 and Xception models for automated walnut species classification. The results underscore the potential of deep learning technologies in improving agricultural practices by offering faster, more accurate, and less labor-intensive alternatives to traditional methods. In comparison, machine learning algorithms such as SVM, RF, and k-NN achieved lower accuracies, with SVM performing best among them at 90.10%. The study provides an important contribution to the use of deep learning technologies in agricultural production processes and suggests solutions that can increase the efficiency of traditional manual methods.