A Comprehensive Survey on Models, Architectures, and Performance Metrics for Medicinal Plant Classification Using Machine Learning and Deep Learning Approaches
Abstract
The exponential growth in multidisciplinary research on medicinal plants has led to a diverse landscape of techniques spanning phytochemical screening, molecular characterization, image-based classification, and machine learning (ML) applications. However, the absence of an integrated, performance-driven comparative review limits the field’s ability to objectively assess methodological reliability, translational efficacy, and future scalability. This study presents a comprehensive review of 80 peer-reviewed papers, systematically evaluating them across eight performance metrics: accuracy, precision, recall, F1-score, IC₅₀, inhibition zone diameter, AUC, and RMSE. Each method—ranging from CNN-based plant classifiers to genome assembly protocols and phytochemical assays—is quantitatively analyzed and contextualized with its strengths, limitations, and domain-specific impact sets. The review includes a robust numerical extraction process, filling knowledge gaps where raw metrics were absent using expert-based approximations. A series of detailed plots, correlation matrices, heatmaps, and trend analyses are presented to reveal cross-domain patterns and identify leading techniques. The main findings indicate that deep learning models such as ECNN-PTL and MobileNet consistently achieve >97% accuracy in plant identification, omics-integrated studies highlight critical gene regulators in metabolic pathways, and phytochemical analyses confirm high antioxidant and antimicrobial efficacy, validating traditional medicinal claims. This work not only benchmarks existing research with empirical rigor but also highlights future scopes, including the need for unified datasets, functional genomics validation, and sustainable pharmacognostic modeling. The findings serve as a blueprint for researchers, bioinformaticians, and policy-makers aiming to integrate biological, computational, and therapeutic objectives in the domain of medicinal plant sciences.
Keywords:
Genome Analysis, Medicinal Plants, Machine Learning, Phytochemistry, Performance Benchmarking
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References
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Shaik, A., Malavath, P., Eswaraiah, P., Papasani, A., & Dhoska, K. (2026). A Comprehensive Survey on Models, Architectures, and Performance Metrics for Medicinal Plant Classification Using Machine Learning and Deep Learning Approaches. International Journal of Advancement in Life Sciences Research, 9(1), 1-31. https://doi.org/https://doi.org/10.31632/ijalsr.2026.v09i01.001
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