Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish
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Abstract
Simple biometric data of fish aid fishery management tasks such as monitoring the structure of fish populations and regulating recreational harvest. While these data are foundational to fishery research and management, the collection of length and weight data through physical handling of the fish is challenging as it is time consuming for personnel and can be stressful for the fish. Recent advances in imaging technology and machine learning now offer alternatives for capturing biometric data. To investigate the potential of deep convolutional neural networks to predict biometric data, several regressors were trained and evaluated on data stemming from the FishL™ Recognition System and manual measurements of length, girth, and weight. The dataset consisted of 694 fish from 22 different species common to Laurentian Great Lakes. Even with such a diverse dataset and variety of presentations by the fish, the regressors proved to be robust and achieved competitive mean percent errors in the range of 5.5 to 7.6% for length and girth on an evaluation dataset. Potential applications of this work could increase the efficiency and accuracy of routine survey work by fishery professionals and provide a means for longer‐term automated collection of fish biometric data.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish |
| Series title | Ecology and Evolution |
| DOI | 10.1002/ece3.6618 |
| Volume | 10 |
| Issue | 17 |
| Year Published | 2020 |
| Language | English |
| Publisher | Wiley |
| Contributing office(s) | Great Lakes Science Center |
| Description | 13 p. |
| First page | 9313 |
| Last page | 9325 |
| Country | United States |
| State | Illinois, Michigan, Ohio |
| Other Geospatial | Black Mallard River, Cheboygan River, Illinois River, Little Manistee River, Menominee River, Muskegon River, Ocqueoc River, Sandusky River, Tittabawassee River |
| Google Analytic Metrics | Metrics page |