Enhancing Real-time Herbal Plant Detection in Agricultural Environments with YOLOv8 - Siahaan, Ranty Deviana; Pardede, Herimanto; Simbolon, Iustisia Natalia; Simbolon, Ivanston; Gultom, Dian Jorgy (Universitas Bina Darma, 2024)
The detection of herbal plants plays a crucial role in the utilization of traditional medicine, particularly in the Toba region of Indonesia. This study aims to develop an Android application capable of real-time detection of herbal plants using the YOLOv8 algorithm. The five types of herbal plants targeted in this study are tempuyung, rimbang, papaya leaves, turmeric leaves, and aloe vera. The research methodology includes the collection of a dataset of herbal plant images, which were then labeled using the Roboflow platform. The YOLOv8 model was trained with this dataset to detect herbal plant objects. After training, the model was exported to TensorFlow Lite and integrated into an Android application. Testing was conducted to evaluate the accuracy and real-time detection performance of the application. The results show that the YOLOv8 model achieved a mean Average Precision (mAP) of 92.4%, with optimal real-time detection capabilities on Android devices. The developed application can quickly and accurately detect and identify herbal plants, providing a practical solution for users to recognize herbal plants. This study indicates that the YOLOv8 algorithm is effective for herbal plant recognition applications in a mobile context, opening up opportunities for further development in the integration of AI technology into everyday applications.