Plant identification in the era of digitalization: opportunities and challenges

Accurate plant identification is a cornerstone of biodiversity conservation, ecological research and environmental management. While traditional taxonomic approaches based on morphological characters and identification keys have long been foundational, they are often time-consuming, error-prone and reliant on expert interpretation. In recent years, digital technologies have begun to reshape the field. Advances in image-based identification using artificial intelligence, particularly deep learning, may enhanced species recognition, despite ongoing challenges related to morphological variation, image quality and environmental conditions. Molecular techniques—such as DNA barcoding, qPCR, LAMP and Nanopore sequencing—offer high-resolution insights, proving especially valuable for identifying cryptic species, authenticating plant-derived products and informing conservation strategies. Meanwhile, remote sensing enables large-scale monitoring of vegetation and is increasingly applied to species-level identification. Together, these tools support an integrative taxonomy that merges morphological, molecular and spatial data to create more robust and scalable identification frameworks. Looking ahead, key priorities include improving interoperability across digital platforms, harnessing high-performance computing and developing AI models capable of learning from limited or imbalanced datasets. While digital tools are becoming more accessible, the role of professional taxonomists remains central in ensuring data interpretation, accuracy and relevance. This technological convergence signals a paradigm shift toward faster, more inclusive and data-driven plant identification.