Med Herb Lens: A Prototype AI App for Medicinal Plant Identification

This article presents Med Herb Lens, a mobile application prototype that uses artificial intelligence to identify medicinal plants. Med Herb Lens does not attempt to identify all plant species, rather, it focuses on plants used for medicinal purposes. The app combines a deep-learning-trained image classifier system with a dynamic, user-augmented knowledge base. EfficientNetB0 was used for model development, with model performance improved via transfer learning. The model was converted into TensorFlow Lite to facilitate real-time and offline inference on Android devices. A Firebase backend architecture is employed to facilitate user contributions, synchronise the dynamic knowledge base, and enable users to access the application in remote areas. The implementation of the proof-of-concept model with a small dataset (seven medicinal plant species) yielded high classification accuracy and inference times of under 400 milliseconds on average. The app could also facilitate crowdsourcing of imagery and metadata, while allowing for iterative model improvement and development, and preserving traditional botanical knowledge. This work suggests that AI-powered tools can bridge the gap between conventional medicine and technology and be implemented in low-resource settings, focusing on under-resourced communities.