Multiclass Herbal Plant Classification Using CNN Architectures: A Comparative Study of MobileNetV2, EfficientNetV2B0, NASNetMobile, and InceptionV3 - Mechi Sakinatun Nufus; Siti Mutmainah; Fathir Fathir (Forum Kerjasama Pendidikan Tinggi, 2026)
Indonesia is a country with an exceptionally rich biodiversity; herbal plants offer a wide range of benefits in the fields of health and traditional medicine. However, the process of identifying herbal leaves is still done manually and is often prone to errors due to similarities in shape, color, and texture among leaves. This study aims to develop a multi-class herbal plant leaf image classification system based on a Convolutional Neural Network (CNN) by comparing four transfer learning architectures: MobileNetV2, EfficientNetV2B0, NASNetMobile, and InceptionV3. The dataset used consists of 10 classes of herbal plant leaves. The contributions of this study include a comparative analysis of four CNN architectures for multi-class classification, an evaluation of the effectiveness of preprocessing and data augmentation on a limited dataset, and recommendations for the most optimal model based on accuracy and computational efficiency. The experimental results show that all models achieved validation accuracy above 98%. InceptionV3 delivered the best performance with a test accuracy of 97%, precision of 90%, and accuracy, recall, and F1-score of 89% respectively, demonstrating good generalization ability. Meanwhile, MobileNetV2 offers the best balance between accuracy and computational efficiency, making it a promising candidate for herbal plant identification systems based on mobile devices or in environments with limited computational resources.