Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish-Reference-Cited by-同舟云学术

Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish

Published:2020-08-04 Issue:17 Volume:10 Page:9313-9325
ISSN:2045-7758
Container-title:Ecology and Evolution
language:en
Short-container-title:Ecol Evol

Author:

Bravata Nicholas¹,Kelly Dylan¹,Eickholt Jesse¹^ORCID,Bryan Janine²,Miehls Scott³,Zielinski Dan⁴

Affiliation:

1. Department of Computer Science Central Michigan University Mount Pleasant MI USA

2. Whooshh Innovations, Inc. Seattle WA USA

3. U.S. Geological Survey Millersburg MI USA

4. Great Lakes Fishery Commission Ann Arbor MI USA

Funder

Central Michigan University

Great Lakes Fishery Commission

Publisher

Wiley

Subject

Nature and Landscape Conservation,Ecology,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.6618

Reference33 articles.

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2. Environmentally driven fluctuations in condition factor of adult Gulf menhaden (Brevoortia patronus) in the northern Gulf of Mexico

3. Image-based, unsupervised estimation of fish size from commercial landings using deep learning

4. The selection of suitable indices for the measurement and analysis of fish condition

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