Digital Herbaria And AI-Based Plant Species Identification: Advancing Botanical Research and Higher Education.

Herbaria have long served as the backbone of plant taxonomy, providing authenticated reference material for species identification, biodiversity research, and botanical education. Despite their continued scientific relevance, traditional herbarium-based identification faces increasing challenges, including limited physical accessibility, time-intensive morphological analysis, and a global decline in taxonomic expertise. These constraints have become more pronounced as the demand for rapid and large-scale biodiversity documentation continues to grow. The digitization of herbarium collections has emerged as a critical response, enabling the long-term preservation of specimens while substantially improving accessibility and data sharing. Concurrently, advances in artificial intelligence, particularly in image-based classification and pattern recognition, have introduced new possibilities for supporting plant species identification using morphological traits captured in digital images. This paper examines how digital herbaria can function as structured and reliable data sources for AI-based species identification systems. The study critically analyses existing image-based identification approaches, focusing on key morphological features such as leaf architecture, venation patterns, and floral characteristics derived from digitized herbarium specimens. The concept of AI-assisted taxonomy is presented as a collaborative framework in which artificial intelligence enhances the efficiency of taxonomic workflows while remaining dependent on expert validation and classical botanical knowledge. The integration of digital herbaria and artificial intelligence offers clear advantages for botanical research and academic training, supporting more efficient species identification while preserving the scientific foundations of traditional taxonomy.