Optimizing image capture for computer vision‐powered taxonomic identification and trait recognition of biodiversity specimens

Abstract Biological collections house millions of specimens with digital images increasingly available through open‐access platforms. However, most imaging protocols were developed for human interpretation without considering automated analysis requirements. As computer vision applications revolutionize taxonomic identification and trait extraction, a critical gap exists between current digitization practices and computational analysis needs. This review provides the first comprehensive practical framework for optimizing biological specimen imaging for computer vision applications. Through interdisciplinary collaboration between taxonomists, collection managers, ecologists and computer scientists, we synthesized evidence‐based recommendations addressing fundamental computer vision concepts and practical imaging considerations. We provide immediately actionable implementation guidance while identifying critical areas requiring community standards development. Our framework encompasses 10 interconnected considerations for optimizing image capture for computer vision‐powered taxonomic identification and trait extraction. We translate these into practical implementation checklists, equipment selection guidelines and a roadmap for community standards development, including filename conventions, pixel density requirements and cross‐institutional protocols. By bridging biological and computational disciplines, this approach unlocks automated analysis potential for millions of existing specimens and guides future digitization efforts towards unprecedented analytical capabilities.