Comparison of Transfer Learning Strategies Using MobileNetV2 and ResNet50 for Ecoprint Leaf Classification - Hajar, Siti; Murinto, Murinto; Yudhana, Anton (Informatika, Universitas Jenderal Soedirman, 2025)
This research focuses on the classification of leaf types used in ecoprint production through the steaming technique by applying transfer learning on two widely recognized convolutional neural network (CNN) architectures, MobileNetV2 and ResNet50. Leaves have diverse applications in various sectors such as medicine, nutrition, and handicrafts. The study utilized a total of 600 leaf images from 15 species were collected from the surrounding environment and divided into 80% training and 20% testing sets. The aim of this study is to classify leaf types suitable for ecoprint quickly and efficiently, based on transfer learning with two CNN architectures, while incorporating fine-tuning. MobileNetV2 was selected for its computational efficiency, while ResNet50 was chosen for its ability to address the vanishing gradient problem and deliver high accuracy. Fine-tuning was employed to optimize model performance. Experimental results demonstrate that both architectures achieved strong performance, with MobileNetV2 reaching 94.12% accuracy and ResNet50 slightly outperforming it at 94.96%. Confusion matrix evaluation further confirmed these results, yielding accuracy, precision, recall, and F1-score values of 0.94, 0.95, 0.95, and 0.94, respectively. These findings highlight ResNet50’s superior performance over MobileNetV2 while affirming the effectiveness of both models in ecoprint leaf classification.