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 emphasizes 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 maximize real-world impact in medicinal plant identification.

Smartphone-based plant identification increasingly serves as the edge tier of agricultural Internet of Things (IoT) systems, where models must adapt to crowdsourced data under bandwidth, memory, and energy constraints. No prior work, to our knowledge, has systematically investigated continual learning at the scale of thousands of fine-grained medicinal plant species from crowdsourced images, nor how retraining frequency affects the cost–performance trade-off in an IoT model-lifecycle setting. We evaluate three continual learning strategies, naïve fine-tuning, experience replay, and Learning without Forgetting, under periodic retraining schedules (updating every K increments), tested on 2719 species (≥25 images each) from the Viet Medi Species 2026 dataset (310,647 images; 4799 species total). All three strategies exhibit negative forgetting (performance improvement rather than degradation) in the instance-incremental setting, with naïve fine-tuning and LwF showing the strongest gains. Periodic retraining with $K=2$ halves retraining operations while maintaining comparable performance. A baseline MobileNetV2 model achieves 54.07% top-10 accuracy across 2719 species and has been deployed via TensorFlow Lite (FP16, ∼11.5 MB) in the Med Herb Lens Android application. In this regime, naïve fine-tuning offers a favourable cost–performance trade-off and is a reasonable default for instance-incremental agricultural IoT deployments.