A Prototype System for Real-Time Monitoring of Arctic Char in Indoor Aquaculture Operations: Possibilities &amp; Challenges

Ramin Soltanzadeh,Robert D Mcleod,Marcia R Friesen,Bruce Hardy

doi:10.1109/access.2020.3028544

Abstract

In this exploratory study, we studied and qualitatively evaluated a prototype video data collection system to capture and analyze fish behavior in a small-scale indoor aquaculture operation. The research objective was to design and develop a hardware / software system that would have the potential to capture meaningful data from which to extract fish size, swim trajectory, and swim velocity, ultimately as information toward an assessment of fish health. The initial work presented in this paper discusses the development choices of the prototype system, including various combinations of lighting and camera positions both inside and outside of the aquaculture tanks, and several post-processing techniques to isolate fish in video, calibrate the distance from camera to fish through water, and infer fish trajectories and swim velocities. Preliminary results provided a qualitative assessment of such a system. Specific results on the system’s ability to detect fishes’ positions, trajectories, and velocities are presently limited to observational outcomes and descriptive statistics rather than large-scale quantitative analysis. The present work lays a foundation for a future commercially hardened system that would be required for the collection of larger datasets, which would in turn facilitate the future development of machine learning (ML) algorithms to begin to statistically correlate data to fish conditions and behaviors in near-real time.

Highlights

Aquaculture has grown into an $800M annual industry in Canada, with thousands of employment opportunities directly related to rearing and holding fish, and indirectly related to associated supply and services industries [1]
Other studies have investigated underwater fish tracking to provide insights into the sensitivity of fish within an environment, which in turn may help promote the welfare of farmed fish
Afterwards, by employing object recognition at each frame, we developed algorithms to track multiple fish

Summary

Introduction

Aquaculture has grown into an $800M annual industry in Canada, with thousands of employment opportunities directly related to rearing and holding fish, and indirectly related to associated supply and services industries [1]. Researchers used a camera mounted on a marine craft to track fish based on the connected components on a fish’s body in each frame [13]. Their tracking of Large Mouth Bass, while mobile, is founded on detecting distinctive dark lines that mark this particular species’ body and tail. Researchers used the covariance algorithm between the frames to identify multiple fish in each frame [14], without the use of any lighting system and limited tracking to a side view in each frame. The covariance algorithm was inspired from vehicle and pedestrian tracking algorithms [15], [16]

Objectives

Methods

Results

Conclusion