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DiversityScanner: Robotic handling of small invertebrates with machine learning methods

Wührl, Lorenz ORCID iD icon; Pylatiuk, Christian; Giersch, Matthias; Lapp, Florian; Rintelen, Thomas; Balke, Michael; Schmidt, Stefan; Cerretti, Pierfilippo; Meier, Rudolf

Abstract:

Invertebrate biodiversity remains poorly understood although it comprises much of the terrestrial animal biomass, most species and supplies many ecosystem services. The main obstacle is specimen-rich samples obtained with quantitative sampling techniques (e.g., Malaise trapping). Traditional sorting requires manual handling, while molecular techniques based on metabarcoding lose the association between individual specimens and sequences and thus struggle with obtaining precise abundance information. Here we present a sorting robot that prepares specimens from bulk samples for barcoding. It detects, images and measures individual specimens from a sample and then moves them into the wells of a 96-well microplate. We show that the images can be used to train convolutional neural networks (CNNs) that are capable of assigning the specimens to 14 insect taxa (usually families) that are particularly common in Malaise trap samples. The average assignment precision for all taxa is 91.4% (75%–100%). This ability of the robot to identify common taxa then allows for taxon-specific subsampling, because the robot can be instructed to only pick a prespecified number of specimens for abundant taxa. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000141105
Veröffentlicht am 27.04.2022
Originalveröffentlichung
DOI: 10.1111/1755-0998.13567
Scopus
Zitationen: 50
Web of Science
Zitationen: 45
Dimensions
Zitationen: 62
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 05.2022
Sprache Englisch
Identifikator ISSN: 1755-098X, 1755-0998
KITopen-ID: 1000141105
HGF-Programm 47.14.02 (POF IV, LK 01) Information Storage and Processing in the Cell Nucleus
Erschienen in Molecular ecology resources
Verlag John Wiley and Sons
Band 22
Heft 4
Seiten 1626-1638
Vorab online veröffentlicht am 04.12.2021
Nachgewiesen in Scopus
Dimensions
Web of Science
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