A Systematic Review of Machine Learning Approaches for Medicinal Plant Classification and Identification

In this paper, a systematic review of Machine Learning (ML) and Deep Learning (DL) technologies in the field of medicinal plant classification and identification is provided. A review of 104 papers (published from 2018 to 2024) was conducted using the PRISMA framework to evaluate datasets, feature extraction processes, model frameworks, and results. The review reveals significant heterogeneity in data quality, image source, preprocessing techniques, and assessment processes, thereby limiting the comparability of procedures across studies. The most popular models are still based on CNNs. Still, new models are also actively researched: Vision Transformers (ViTs) and Graph Neural Networks (GNNs) have the potential to consider global dependencies and relational structure. These problems have been identified as the unavailability of data to the wider society, non-uniformity in labelling, and the inability to interpret deep models. Overall, CNN-based models, especially hybrid and ensemble models, show the highest consistency in classification accuracy across studies in the reviewed papers and provide evidence of their current prevalence in the classification of medicinal plants. Still, this effect is highly dataset-dependent, underscoring the need for standardized datasets, integrated evaluation procedures, and interpretable AI methods to enable reliable clinical implementation.