Classification of Herbal Plant Images Using Transfer Learning EfficientNetV2-B3

Abstract

Herbal plants are natural resources that have high economic and health value, but the identification process is still done manually, making it prone to errors due to morphological similarities between species. This study aims to develop a leaf image classification model for herbal plants using a Convolutional Neural Network (CNN) with the EfficientNetV2-B3 transfer learning approach and AdamW optimizer. The dataset used is the Indonesian Herb Leaf Dataset 3500, which consists of 3,500 leaf images from 10 types of Indonesian herbal plants, namely Belimbing Wuluh, Jambu Biji, Jeruk Nipis, Kemangi, Lidah Buaya, Nangka, Pandan, Pepaya, Seledri, and Sirih. The research stages included preprocessing, dataset division, and augmentation such as flipping, rotation, zooming, contrast and brightness changes, translation, and the addition of Gaussian noise and salt-and-pepper noise to increase data variation and test model robustness. Evaluation based on accuracy, precision, recall, and f1-score shows that the model without augmentation achieved 98.57% accuracy, 98.63% precision, 98.57% recall, and a 98.58% f1-score, while the model with augmentation and noise addition achieved an accuracy of 97.71%, precision of 97.83%, recall of 97.71%, and an f1-score of 97.72%. These results prove that EfficientNetV2-B3 is capable of effectively classifying herbal plant leaves with good performance.