Deep Learning for Intelligent Classification of Medicinal Plants Using Cell Wall Component and Metabolomic Features
Keywords:
medicinal plant classification; deep learning; metabolomics; cell wall components; multimodal fusion; system architecture; governance; quality assuranceAbstract
The reliable classification of medicinal plants is essential for pharmaceutical quality control, botanical safety, and regulatory compliance. Traditional morphological identification is increasingly complemented by biochemical profiling based on cell wall components and metabolomic signatures. However, these data sources are heterogeneous, high dimensional, and often noisy, which limits the effectiveness of classical statistical classifiers. Deep learning offers a powerful framework for discovering multilayered representations across such multimodal chemical and structural features. This paper examines the design of intelligent classification systems for medicinal plants from a systems-oriented perspective. It addresses the integration of cell wall component profiles and metabolomic data, the selection of appropriate deep learning architectures, and the infrastructure required for reproducible model development and deployment. The discussion emphasizes structural trade-offs among model capacity, interpretability, data heterogeneity, computational cost, and regulatory readiness. Rather than focusing narrowly on algorithmic performance, the paper analyzes the broader socio-technical context, including data governance, fairness across underrepresented plant taxa, robustness under environmental and instrumental variation, and the sustainability of classification infrastructures. The paper further considers deployment pathways in botanical research, herbal supply chains, and pharmacovigilance. It argues that intelligent medicinal plant classification should be designed as a governed, auditable, and multimodal learning system rather than as an isolated pattern recognition task. Future directions include federated model training, standardized spectral and metabolomic exchange formats, uncertainty-aware inference, and closer alignment with regulatory frameworks for botanical identification and quality assurance.
References
1. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
2. Fiehn, O. (2002). Metabolomics—the link between genotypes and phenotypes. Plant Molecular Biology, 48(1–2), 155–171.
3. Sumner, L. W., Mendes, P., & Dixon, R. A. (2003). Plant metabolomics: Large-scale phytochemistry in the functional genomics era. Phytochemistry, 62(6), 817–836.
4. Wolfender, J.-L., Rudaz, S., Choi, Y. H., & Kim, H. K. (2013). Plant metabolomics: From holistic data to relevant biomarkers. Current Medicinal Chemistry, 20(8), 1056–1090.
5. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
6. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).
7. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
8. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
9. van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.
10. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
11. Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In International Conference on Learning Representations.
12. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., & Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (pp. 265–283).
13. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., & Chintala, S. (2019). PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32, 8024–8035.
14. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J. W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., 't Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S. A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., & Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.
15. Zhao, K., Li, G., Li, C., Jiang, T., Wang, W., & Yang, Z. (2026). Identification Markers for Salvia miltiorrhiza and Its Close Relatives Based on Cell Wall Component Characteristics. Engineered Science, 40, 2122.
16. Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 618–626).
17. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220–229).
18. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).
19. Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine, 169(12), 866–872.
20. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
21. Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160.
22. Chollet, F. (2021). Deep learning with Python (2nd ed.). Manning Publications.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Clinical and Translational Medicine
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
