A Systematic Review of Medicinal Plant Identification Using Deep Learning

Medicinal plants fulfil critical global health needs, but reliable identification poses barriers. This systematic review analyses 30 studies on deep learning for automated medicinal plant species classification to offer real-world insights. Convolutional neural networks (CNNs) demonstrate over 90% testing accuracy on plant organs such as leaves and flowers, enabling precise recognition models. While increasing species diversity and the use of crowdsourced data may pose performance challenges, optimisation strategies such as data augmentation and ensemble models may help mitigate accuracy declines. It appears that plant states (fresh vs. dry/sliced) may impact the model performance, although some models could distinguish maturity stages with sufficient data. Across geographical regions, CNNs show strong local identification capabilities, but generalised global models require larger, inclusive datasets. While mobile apps provide practical deployment avenues, robust mechanisms for continuous user-driven refinement are lacking. Ultimately, it appears that context-conscious deep learning approaches balancing efficiency and representation across diverse contexts are imperative for maximising real-world impact. This timely review consolidates evidence to guide the responsible development of specialised medicinal plant recognition systems.