Published Paper Details:

Plant taxonomy, the foundation of botanical science, is entering a transformative era driven by the integration of artificial intelligence (AI) and multidisciplinary data. Traditional taxonomy has relied heavily on morphological traits, but the complexity of plant diversity and cryptic species often challenges human-based identification. Integrative taxonomy, which combines morphological, molecular, ecological, and geographical data, provides a more holistic framework for species delimitation. However, handling and interpreting such heterogeneous data demand computational methods capable of recognizing complex patterns and relationships. Here, we present an overview of how AI particularly machine learning and deep learning can revolutionize plant taxonomy by automating data analysis, detecting hidden diversity, and accelerating species identification. We highlight the integration of image-based recognition of plant organs, DNA barcoding classification, and ecological niche modelling through AI algorithms. Additionally, we discuss recent advances in multimodal data fusion that enable the synthesis of molecular and phenotypic datasets for more robust taxonomic decisions. The study emphasizes the potential of AI to enhance reproducibility, reduce human bias, and enable rapid biodiversity assessment in the face of global environmental change. We conclude that the synergy between integrative taxonomy and artificial intelligence represents a paradigm shift in plant systematics, paving the way for a new era of automated, data-driven taxonomy and biodiversity discovery.