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.

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Publication Details

Journal
Methods in Ecology and Evolution
Published
2025-09-03
DOI
https://doi.org/10.1111/2041-210x.70140
Citations
4
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
7.35

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article

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

Pramod Kumar Singh, Isadora E. Fluck, Hilmar Lapp, Kim J. Landsbergen et al.
4 citations
Methods in Ecology and Evolution
Species Distribution and Climate Change
7.35
article

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

Pramod Kumar Singh, Isadora E. Fluck, Hilmar Lapp, Kim J. Landsbergen, Iuliia Zarubiieva, Paula Mabee, Alyson East, Ekaterina Nepovinnykh, Nathan Fox, Elizabeth Campolongo, Kayla I. Perry, Charles V. Stewart, Scott Lowe, Matthew B. Thompson, Isabelle Betancourt, Noah Charney, Khum Bahadur Thapa‐Magar, Tanya Berger‐Wolf, Sydne Record, Jennifer C. Girón, Evan Waite, S M Rayeed, Michelle Ramírez, Maximiliane Jousse, Samuel Stevens, Graham W. Taylor, Luke Meyers, E Donoso
article en
4 citations

Abstract

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.

Methods in Ecology and EvolutionVol. 16(10)
Texas Tech University (US), Rensselaer Polytechnic Institute (US), University of Puerto Rico at Río Piedras (PR), Duke University (US), University of Colorado Boulder (US), University of Michigan (US), National Ecological Observatory Network (US), University of Florida (US), Michigan United (US), Vector Institute (CA), McGill University (CA), The Ohio State University (US), Arizona State University (US), University of Maine (US), University of Guelph (CA), Antioch College (US)
National Science Foundation, U.S. Department of Agriculture, Battelle, Arkansas NSF EPSCoR, National Institute of Food and Agriculture, Natural Sciences and Engineering Research Council of Canada, Office of International Science and Engineering, Office of Advanced Cyberinfrastructure, Office of Experimental Program to Stimulate Competitive Research
Openalex Percentile: Top 3%
Species Distribution and Climate Change
7.35
DOIOpen Access PDFOpenAlex
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