Fine-Tuning MobileNet for Durian Variety Classification

Durian, often referred to as the king of fruits, is widely consumed in Southeast Asia. However, the classification of its varieties is complicated by the lack of distinct visual differences between them. In this study, a fine-tuned MobileNet, a lightweight deep learning model, is applied for the classification of durian varieties. Transfer learning techniques are employed to adapt the MobileNet architecture using a custom dataset of durian images, enabling accurate differentiation between multiple varieties. First, the original MobileNet model is evaluated, which is found to yield low accuracy (8.22%) and a high loss (2.0553). A durian-specific classification layer is then added, and the model is trained for 100 epochs (2 min 21 s), achieving 76.28% training accuracy (0.5985 loss) and 73.20% validation accuracy (0.7606 loss). Further fine-tuning is performed, resulting in 100% training accuracy (4.9623 × 10−4 loss) and 93.69% validation accuracy (0.2281 loss) after 100 epochs (3 min 55 s). The findings demonstrate that the fine-tuned MobileNet model is capable of high classification accuracy while maintaining computational efficiency, making it suitable for real-time durian variety identification in agricultural and commercial settings.