This article presents a non-invasive fully automatic procedure for Bluefin Tuna sizing, based on a stereoscopic vision system and a deformable model of the fish ventral silhouette. An image processing procedure is performed on each video frame to extract individual fish, followed by a fitting procedure to adjust the fish model to the extracted targets, adapting it to the bending movements of the fish. The proposed system is able to give accurate measurements of tuna snout fork length (SFL) and widths at five predefined silhouette points without manual intervention. In this work, the system is used to study size evolution in adult Atlantic Bluefin Tuna (Thunnus Thynnus) over time in a growing farm. The dataset is composed of 12 pairs of videos, which were acquired once a month in 2015, between July and October, in three grow-out cages of tuna aquaculture facilities on the west Mediterranean coast. Each grow out cage contains between 300 and 650 fish on an approximate volume of 20 000 m³. Measurements were automatically obtained for the 4 consecutive months after caging and suggest a fattening process: SFL shows an increase of just a few centimetres (2%) while the maximum width (A₁) shows a relative increase of more than 20%, mostly in the first 2 months in farm. Moreover, a linear relation (with coefficient of determination R² > 0.98) between SFL and widths for each month is deduced, and a fattening factor (F) is introduced. The validity of the measurements is proved by comparing 15 780 SFL measurements, obtained with our automatic system in the last month, versus ground truth data of a high percentage of the stock under study (1143 out of 1579), obtaining no statistically significant difference. This procedure could be extended to other species to assess the size distribution of stocks, as discussed in the article.

Posidonia oceanica meadows are ecosystem engineers that play several roles in marine environment maintenance. In this sense, monitoring of the spatial distribution and health status of their meadows is key to make decisions about protecting them against their degradation. With the aim of checking the ability of a simple low-cost acoustic method to acquire information about the state of P. oceanica meadows as ecosystem indicators, ground-truthing and acoustic data were acquired over several of these meadows on the Levantine coast of Spain. A 200 kHz side scan sonar in a vertical configuration was used to automatically estimate shoot density, canopy height and cover of the meadows. The wide athwartship angle of the transducer together with its low cost and user friendliness entail the main advantages of this system and configuration: both improved beam path and detection invariance against boat rolling. The results show that canopy height can be measured acoustically. Furthermore, the accumulated intensity of the echoes from P. oceanica in the first 30 centimeters above the bottom is indirectly related to shoot density and cover, showing a relation that should be studied deeply.

Underwater noise has been identified as a relevant pollution affecting marine ecosystems in different ways. Despite the numerous studies performed over the last few decades regarding the adverse effect of underwater noise on marine life, a lack of knowledge and methodological procedures still exists, and results are often tentative or qualitative. A monitoring methodology for the behavioral response of bluefin tuna (Thunnus thynnus) when exposed to ship and wind turbine operational noises was implemented and tested in a fixed commercial tuna feeding cage in the Mediterranean sea. Fish behavior was continuously monitored, combining synchronized echosounder and video recording systems. Automatic information extracted from acoustical echograms was used to describe tuna reaction to noise in terms of average depth and vertical dimensions of the school and the indicators of swimming speed and tilt direction. Video recordings allowed us to detect changes in swimming patterns. Different kinds of stimuli were considered during bluefin tuna cage monitoring, such as noise generated by feeding boats, wind farm operational noise, and other synthetic signals projected in the medium using a broadband underwater projector. The monitoring system design was revealed as a successful methodological approach to record and quantify reactions to noise. The obtained results suggested that the observed reactions presented a strong relationship with insonification pressure level and time. Behavioral changes associated with noise are difficult to observe, especially in semi-free conditions; thus, the presented approach offered the opportunity to link anthropogenic activity with possible effects on a given marine species, suggesting the possibility of achieving a more realistic framework to assess the impacts of underwater noise on marine animals.

This study presents various models based on formulae relating weight and dimensions (length, height and width) of Bluefin tuna, Thunnus thynnus (L.), fattened in captivity. The main aim of establishing these expressions is to design tools for indirectly predicting the weight of a Bluefin tuna from measurements of one or more dimensions obtained using non-invasive methods such as stereoscopic cameras. Measurements of maximum length, height and width following slaughter were taken of fish fattened in captivity (n = 2078). Different relationships drawn from the dimensions of the tuna against their weight are fitted with part of the data collection and later checked against a reserved sample set. The resulting formulae are compared with the formulae most commonly used in the case of wild tuna. The results of this study confirm that, for tuna fattened in cages, the availability of more than one dimension to estimate weight improves the predictive power of the model and reduces error in the estimate.

A proposal is described for an underwater sensor combining an acoustic device with an optical one to automatically size juvenile bluefin tuna from a ventral perspective. Acoustic and optical information is acquired when the tuna are swimming freely and the fish cross our combined sensor’s field of view. Image processing techniques are used to identify and classify fish traces in acoustic data (echogram), while the video frames are processed by fitting a deformable model of the fishes’ ventral silhouette. Finally, the fish are sized combining the processed acoustic and optical data, once the correspondence between the two kinds of data is verified. The proposed system is able to automatically give accurate measurements of the tuna’s Snout-Fork Length (SFL) and width. In comparison with our previously validated automatic sizing procedure with stereoscopic vision, this proposal improves the samples per hour of computing time by 7.2 times in a tank with 77 juveniles of Atlantic bluefin tuna (Thunnus thynnus), without compromising the accuracy of the measurements. This work validates the procedure for combining acoustic and optical data for fish sizing and is the first step towards an embedded sensor, whose electronics and processing capabilities should be optimized to be autonomous in terms of the power supply and to enable real-time processing.