Towards intelligent aquaculture. Development of an early Biological Warning System to monitor exposure to contaminants and fish welfare: from artificial vision to systems modelling

The annual global increase in the production, particularly from aquaculture, and consumption of seafood is expected to continue in the future. One of the main concerns is the production of safe seafood, which is being compromised by the increasing number of novel and unexpected chemical substances that contaminate the aquatic environment and may end up contaminating also the feeds used in aquaculture. Many of these contaminants also affect negatively the fish welfare and consequently carry economic losses for the farmer. Currently, there is a lack of cost effective, user-friendly methods to detect many of these contaminants and there is no method to detect unknown contaminants. The present Thesis aims at developing a non-invasive monitoring methodology using the fish themselves as the sensor unit; i.e. a Biological Warning System. This kind of approach would make it possible to develop an affordable, on-line identification of production units displaying atypical responses or behaviour due to stress factorsand therefore potentially contaminated regardless of whether the contaminant is an identified or an unknown substance. Once developed, the Biological Warning System should be implemented within the Hazard Analysis and Critical Control Point and Fish Welfare Assurance Systems and the results accompany the traceability documentation of the products. The first phase of this Thesis comprises the development of a non-invasive methodology for image acquisition, image processing and non-linear signal analysis and processing of the collective fish response to a stochastic event, using the collective fish response, i.e., the response of the shoal. Object detection and motion estimation were performed by an optical flow algorithm in order to detect moving fish and simultaneously eliminate background, noise and artifacts. The entropy and the fractal dimension of the trajectory followed by the centroids of the groups of fish were calculated using the Shannon and permutation entropy and the Katz, Higuchi and Katz-Castiglioni’s fractal dimension algorithms respectively. The tool developed was tested in three case studies. The first case study analyzed three experimental groups of European seabass (Dicentrarchus labrax), two of which were similar (control and tagged fish) and very different from the third [tagged fish submerged in methylmercury (4 μg MeHg/L) contaminated water for 9 days]. The results indicate that Shannon entropy and Katz-Castiglioni were the most sensitive non-linear signal processing algorithms, both with the potential to become useful tools for the non-invasive identification and quantification of differences in fish responses, although the Katz-Castiglioni fractal algorithm had a much higher computational load than the Shannon entropy algorithm. The number of individuals in the shoal was, a priori, expected to exert an influence in the behaviour of the system. Accordingly, the second case study was designed to identify the effect of the number of fish on the “normal” behaviour of the system, which in turn requires the characterization of the Optimal Working Point/Range