EARLY HEARING DETECTION AND INTERVENTION VIRTUAL CONFERENCE
MARCH 2-5, 2021

(Virtually the same conference, without elevators, airplane tickets, or hotel room keys)

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6/08/2017 | 11:15 AM - 11:30 AM | AUTOMATED ASSESSMENT OF DAPHNIA MAGNA POPULATIONS USING DIGITAL IMAGING AND MACHINE LEARNING | 306C

AUTOMATED ASSESSMENT OF DAPHNIA MAGNA POPULATIONS USING DIGITAL IMAGING AND MACHINE LEARNING

Daphnia magna is a species of water flea often used to assess effects of natural changes or anthropromorphic influences in the environment. The small size and high fecundity of daphnids make them ideal for performing experiments at the organismal and population levels. Currently, the inability to efficiently count individual daphnids within a population hinders the rapid acquisition of longitudinal population-level data, thereby limiting the causal association between organismal responses and ecosystem adversity. We propose a novel method to semi-automate population-level data collection of daphnids. We use a document scanner and 3-d printed chambers to produce images of samples from daphnid populations. Using machine learning, specifically deep neural networks, we are able to count the daphnids present in each image with over 98% accuracy. We validate our results with a 3-week population study examining the effect of the herbicide pendimethalin on daphnid populations. We compare the results and scalability of our automated method with hand-counting, in terms of both accuracy and total counting time.

S25 Do mesocosm experiments play a meaningful role in freshwater research
C03 Invertebrates
C03 Invertebrates

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Presenters/Authors

Erica Rutter (), North Carolina State University, erutter@ncsu.edu;