Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish
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.
Citation Information
| Publication Year | 2020 |
|---|---|
| Title | Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish |
| DOI | 10.1002/ece3.6618 |
| Authors | Nicholas Bravata, Dylan Kelly, Jesse Eickholt, Janine Bryan, Scott M. Miehls, Daniel Zielinski |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Ecology and Evolution |
| Index ID | 70216944 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Great Lakes Science Center |
Related Content
Image and biometric data for fish from Great Lakes tributaries collected during spring 2019
Related Content
- Data
Image and biometric data for fish from Great Lakes tributaries collected during spring 2019
Image and biometric data were collected for 22 species of fish from Great Lakes Tributaries in Michigan and Ohio, and the Illinois River for the purpose of developing a fish identification classifier. Data consists of a comma delimited spreadsheet that identifies image file names and associated fish identification number, common name, species code, family name, genus, and species, date collected, - Connect