Advancements in Medicinal Plant Identification Using Deep Learning Techniques: A Comprehensive Review

Deep learning has emerged as a transformative approach in medicinal plant identification, addressing the critical need for accurate and scalable solutions to support biodiversity conservation, traditional medicine, and sustainable healthcare practices. This systematic literature review examines 30 papers on deep learning for medicinal plant identification, revealing diverse approaches across global contexts. Convolutional neural networks emerge as the primary technique, achieving high accuracy, particularly with leaf-based identification. Data collection methods vary, with manual fieldwork predominating. The review highlights challenges in scaling to larger species sets and using crowdsourced data, though strategies like data augmentation show promise. Plant state and maturity impact model performance, warranting further investigation. The geographical distribution of studies emphasises the global relevance of this research, with India and China contributing the most. Mobile applications offer potential for deployment and data collection but lack robust user feedback mechanisms for model refinement. The review identifies gaps in continuous model updating and suggests exploring incremental and zero-shot learning. Overall, the field shows promise but requires more balanced datasets and context-aware approaches to maximise real-world impact in medicinal plant identification.