OPTIMIZATION OF THE INCEPTIONV3 ARCHITECTURE FOR POTATO LEAF DISEASE CLASSIFICATION - Nur, Khairun Nisa Arifin; Addyna, Nazlina Izmi; Windarto, Agus Perdana; Wanto, Anjar; Poningsih, Poningsih (JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), 2025)
Potato leaf diseases can cause significant yield losses, making early detection crucial to prevent major damages. This study aims to optimize the Inception V3 architecture in a Convolutional Neural Network (CNN) for potato leaf disease classification by applying Fine Tuning Pre-Trained. This method leverages weights from a pre-trained model on a large-scale dataset, enhancing accuracy while reducing the risk of overfitting. The training process involves adjusting several final layers of Inception V3 to better adapt to specific features of potato leaf diseases. The results show that this approach improves classification performance, achieving an accuracy of 97.78%, precision of 98%, recall of 98%, and an F1-score of 98%. With better computational efficiency compared to previous architectures, this model is expected to be widely applicable in plant disease detection systems, particularly for farmers or institutions with limited resources.