Deep Learning-Based Herbal Plant Classification Using Leaf Shape and Pattern: The UII Botanical Leaf Dataset

Herbal plants play a crucial role in healthcare and are widely used as traditional medicines. However, identifying herbal species remains a major challenge due to morphological similarities, particularly in leaf shape and texture. This study aims to develop an intelligent classification system for Indonesian herbal plants based on leaf image analysis using artificial intelligence (AI) and digital image processing techniques. A localized dataset of 47 herbal species collected from Botanical SmartPark SMA UII was used to train a deep learning model employing the MobileNetV2 architecture through transfer learning. The proposed model achieved an average accuracy of 96.6% on the testing dataset, demonstrating high reliability in recognizing species with complex visual variations. The trained model was then implemented into an Android-based application called HERBfull Botanical SmartPark, enabling real-time plant identification and interactive access to botanical information. The system successfully enhances efficiency, accessibility, and educational value in the identification of local herbal species. This research contributes to the advancement of AI applications in botanical education, promoting digital literacy, biodiversity conservation, and the integration of smart technology into sustainable environmental learning platforms.