Abstract: Plant leaves are another common source of information for determining plant species. According to the dataset that has been collected, we propose transfer learning models VGG16, VGG19, and MobileNetV2 to examine the distinguishing features to identify medicinal plant leaves. We also improved algorithm using fine-tuning strategy and analyzed a comparison with and without a fine-tuning strategy to transfer learning models performance. Several protocols or steps were used to conduct this study, including data collection, data preparation, feature extraction, classification, and evaluation. The distribution of training and validation data is 80% for training data and 20% for validation data, with 1500 images of thirty species. The testing data consisted of a total of 43 images of 30 species. Each species class consists of 1-3 images. With a validation accuracy of 96.02 percent, MobileNetV2 with fine-tuning had the best validation accuracy. MobileNetV2 with fine-tuning also had the best testing accuracy of 81.82%.

Vina Ayumi, Ermatita Ermatita, Abdiansah Abdiansah, Handrie Noprisson, Yuwan Jumaryadi, Mariana Purba, Marissa Utami and Erwin Dwika Putra, “Transfer Learning for Medicinal Plant Leaves Recognition: A Comparison with and without a Fine-Tuning Strategy” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130916

@article{Ayumi2022,
title = {Transfer Learning for Medicinal Plant Leaves Recognition: A Comparison with and without a Fine-Tuning Strategy},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130916},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130916},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Vina Ayumi and Ermatita Ermatita and Abdiansah Abdiansah and Handrie Noprisson and Yuwan Jumaryadi and Mariana Purba and Marissa Utami and Erwin Dwika Putra}
}