Classification of Pistachio Varieties Using MobileNet Deep Learning Model

Pistachio, a flowering plant from the Anacardiaceae family, is categorized into various types based on its physical characteristics. Due to its high market value and nutritional importance, accurate identification and packaging based on the pistachio variety are essential for addressing export challenges. Pistachio classification is often performed by electromechanical machines, but these machines lack the necessary precision and can damage the pistachio kernels. Therefore, there is a growing demand for new technologies to improve pistachio classification and separation. In this study, we used a modified version of the MobileNetV3 deep learning model to identify different pistachio varieties. Additionally, by leveraging the Small version of MobileNet, we can efficiently deploy the trained model on smartphones, as it is optimized for computational efficiency. The research was conducted using a dataset of 2148 images representing the Kerman and Sirt pistachio varieties. To increase the number and diversity of images, data augmentation techniques were applied. This helps prevent overfitting and enables the model to generalize better to unseen data. Our modified MobileNetV3 model achieved an accuracy of 99.30% in identifying the two pistachio varieties, outperforming existing classification methods.