AI-Assisted Chemotaxonomic Identification of Salvia Species Based on Cell Wall and Phytochemical Profiles
Keywords:
Salvia identification, chemotaxonomy, cell wall profiling, phytochemical fingerprinting, artificial intelligence, machine learning governance, explainability, system architectureAbstract
The identification of Salvia species is a central problem in medicinal plant authentication, biodiversity monitoring, and phytochemical standardization. Traditional morphological approaches are often insufficient for closely related taxa, while chemical profiling offers diagnostic information that can be difficult to interpret at scale. This paper examines AI-assisted chemotaxonomic identification based on integrated cell wall component profiles and phytochemical fingerprints. Rather than focusing solely on classifier performance, the discussion adopts a system-level perspective that encompasses data acquisition, multimodal fusion, model architecture, technical debt, governance, robustness, fairness, deployment, sustainability, and policy alignment. The paper argues that reliable identification depends not only on algorithmic capacity but also on the design of surrounding infrastructure for data provenance, model auditing, and human oversight. It reviews architectural trade-offs between centralized and distributed deployment, the role of interpretability methods in supporting domain expert judgment, and the risks of bias arising from unrepresentative training collections. Cross-domain lessons from responsible machine learning in health care and ecological monitoring are used to evaluate governance strategies for botanical identification systems. The analysis highlights the need for voucher-linked digital reference libraries, structured dataset documentation, prospective validation, and policy frameworks that respect international biodiversity governance obligations. The paper concludes that AI-assisted chemotaxonomic identification can be scientifically valuable and institutionally sustainable when technical design is integrated with governance arrangements and long-term capacity building.
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