Measuring inter-individual differences in behavioural types of gilthead seabreams in the laboratory using deep learning

PeerJ. 2022 May 5:10:e13396. doi: 10.7717/peerj.13396. eCollection 2022.

Abstract

Deep learning allows us to automatize the acquisition of large amounts of behavioural animal data with applications for fisheries and aquaculture. In this work, we have trained an image-based deep learning algorithm, the Faster R-CNN (Faster region-based convolutional neural network), to automatically detect and track the gilthead seabream, Sparus aurata, to search for individual differences in behaviour. We collected videos using a novel Raspberry Pi high throughput recording system attached to individual experimental behavioural arenas. From the continuous recording during behavioural assays, we acquired and labelled a total of 14,000 images and used them, along with data augmentation techniques, to train the network. Then, we evaluated the performance of our network at different training levels, increasing the number of images and applying data augmentation. For every validation step, we processed more than 52,000 images, with and without the presence of the gilthead seabream, in normal and altered (i.e., after the introduction of a non-familiar object to test for explorative behaviour) behavioural arenas. The final and best version of the neural network, trained with all the images and with data augmentation, reached an accuracy of 92,79% ± 6.78% [89.24-96.34] of correct classification and 10.25 ± 61.59 pixels [6.59-13.91] of fish positioning error. Our recording system based on a Raspberry Pi and a trained convolutional neural network provides a valuable non-invasive tool to automatically track fish movements in experimental arenas and, using the trajectories obtained during behavioural tests, to assay behavioural types.

Keywords: Deep learning; Faster R-CNN; Fish behavioural ecology; Fish tracking; Sparus aurata.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Deep Learning*
  • Individuality
  • Neural Networks, Computer
  • Sea Bream*

Grants and funding

This project was funded by the research project FISHOBES (grant no. CTM2017-91490-EXP) funded by the Spanish Ministry of Science and Innovation (MICINN). Marco Signaroli was suppoerted by a “Ayudas para contratos predoctorales” (grant no. PRE2020-095580) funded by MCIN/AEI /10.13039/501100011033 and the FSE “invierte en tu futuro”. Josep Alós received funding from a Ramon y Cajal Grant (grant no. RYC2018-024488-I), the CLOCKS I+D+I project (grant no. PID2019-104940GA-I00) and JSATS PIE project (grant no. PIE202030E002) funded by MCIN/AEI/10.13039/501100011033 and the FSE “invierte en tu futuro”. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.