Year: 2024
Type: article
Abstract: The taxonomic identification of organisms from images is an active research area within the machine learning community. Current algorithms are very effective for object recognition and discrimination, but they require extensive training datasets to generate reliable assignments. This study releases 5.6 million images with representatives from 10 arthropod classes and 26 insect orders. All images were taken using a Keyence VHX-7000 Digital Microscope system with an automatic stage to permit high-resolution (4K) microphotography. Providing phenotypic data for 324,000 species derived from 48 countries, this release represents, by far, the largest dataset of standardized arthropod images. As such, this dataset is well suited for testing the efficacy of machine learning algorithms for identifying specimens to higher taxonomic categories.
Source: Data
DOI: 10.3390/data9110122
Authors: D Steinke, S Ratnasingham, J Agda, H Ait Boutou, I Box, M Boyle, D Chan, C Feng, SC Lowe, JTA McKeown, J McLeod, A Sanchez, I Smith, S Walker, CY-Y Wei, PDN Hebert
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,819
Funders: Genome Canada, Forest Conservation Fund, Polar Knowledge Canada, Ontario Genomics, Guanacaste Dry Forest Conservation Fund, Ministero dello Sviluppo Economico, Canada First Research Excellence Fund, Ontario Ministry of Economic Development, Job Creation and Trade, Ministry of Colleges and Universities
Awards: G60974634, G7191781591, G735094526
OpenAlex ID: W4403764051