# Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach
## 1 Introduction  
Our motivation in conducting this research was to solve specific challenges that hinder sustainable, intelligent, and precise aquaculture by combining stereoscopic vision techniques and automatic computer vision methods that allow us to extract information from large volumes of data while fish are swimming freely. The large dimensions of oceans and seas make observing and monitoring the marine environment a titanic job. However, many countries are proposing sustainability guidelines and as part of those, they are making these efforts in specific ecosystems, even though they require great human and technological input. Recently, fish farmers, ecologists, and governments have remarked on the urgent need to accurately estimate both the biomass of schools and individual fish in their natural environment [1]. With that goal, the collection of numerous accurate data on fish size or age without the need to physically handle live fish has been identified as an essential requirement. Traditional methods based on manual measurements are invasive, expensive, and stressful for animals, which limit the amount of data collected, reducing the validation of the tests performed. Instead, the quantitative estimation of fish biomass forms the basis of scientific fishery management and conservation strategies [2; 3]. Biomass estimations are an important input for the design of adequate models for the assessment of fisheries, to explore fish growth stages, define growth models, and evaluate the health status of the fish. Species such as tuna and salmon are the most commonly farmed due to their market acceptance and rapid growth [4]. Bluefin tuna (BFT) is one of the most in-demand fish species in the world. Currently, the need to strengthen its management has increased research on BFT aquaculture production in many countries [5]. The ICCAT (International Commission for the Conservation of Atlantic Tunas) recommends using stereoscopic vision systems to size live fish in order to control catches for tuna farming. Commercial stereoscopic systems such as AQ1 AM100 [6] and AKVAsmart, formerly VICASS [7], require human intervention to mark the snout and fork of each fish in the image, from which to deduce the fish length and weight. This slows the process, makes it laborious, introduces the variability of manual measuring, and limits the number of samples that can be gathered to statistically represent the fish stock. The need to develop fully automatic solutions has been pointed out in [8; 9], among others. In these solutions, fish weight may be deduced from fish length, as established in [10]. However, recent works [11; 12] have attempted to find better approximations for their weight by using both fish length and width measurements. They work on the hypothesis that a better estimation of the biomass can be achieved by using more dimensions of the tuna than only length. A significant contribution towards a commercial system for fully automatic fish sizing using stereoscopic vision was proposed in [13], based on geometric models of tuna that are adapted to the fish silhouette while swimming, which can provide accurate measurements of fish length in adult tuna. An extension of this proposal is presented in [14], in which new features are added to the deformable model of tuna so that it is capable of estimating the width of individuals in ventral-perspective images. In their conclusion, the authors comment that the system could be used to track the growth of fish over time by programming a recording schedule, and it could be integrated into an autonomous monitoring system, whose computing performance and energy requirements should be dimensioned to allow the recording and analysis of a statistically representative number of measurements. Working with stereoscopic video requires the joint processing of two frames for each instant of time acquired. In addition, the fish, the object we wish to detect and measure, are swimming freely, which means we do not know when they are in front of our acquisition system.