Atlantic mackerel (Scomber scombrus) change skin colour in response to crowding stress.

Wild capture can be stressful for fish. Stress has the potential to induce mortality in released unwanted catches or negative flesh quality consequences in retained ones. Such effects compromise sustainable natural resource management and industry profitability. Mitigating stress during capture is therefore desirable. Biological indicators of stress can objectively inform fishers as to the functional welfare status of catches during fishing operations. If they are to be of practical use in mitigating stress during wild capture events, such indicators must be quantifiable, respond rapidly, reflect the level of induced stress and be easily observable. Atlantic mackerel (Scomber scombrus) are extensively targeted by purse seine fisheries in European waters but are particularly vulnerable to stress. Excessive crowding in the net is thought to be the principal stress mechanism. There is therefore a need to develop indicators of crowding stress for this species so that catch welfare can be improved. Here, we demonstrate that S. scombrus exhibit a skin colour change from predominately green to predominately blue when exposed to crowding stress. In sea cage trials, we induced various degrees of stress in groups of wild-caught S. scombrus by manipulating crowding density and its duration. Skin colour was quantified in air using digital photography. The colour change occurred rapidly (within the typical duration of crowding events in the fishery), and its magnitude was correlated to the severity and duration of crowding. Bluer fish were also associated with higher levels of plasma lactate. No appreciable colour change was observed in uncrowded (control) groups during the treatment period. Nonetheless, unstressed S. scombrus did turn blue <1 h after death. Together, these results indicate that skin colour change has the potential to be a useful real-time indicator of crowding stress for S. scombrus and could therefore be used to improve welfare during wild capture fishing.

Identification of spectral signature for in situ real-time monitoring of smoltification.

We describe the use of an optical hyperspectral sensing technique to identify the smoltification status of Atlantic salmon (Salmo salar) based on spectral signatures, thus potentially providing smolt producers with an additional tool to verify the osmoregulatory state of salmon. By identifying whether a juvenile salmon is in the biological freshwater stage (parr) or has adapted to the seawater stage (smolt) before transfer to sea, negative welfare impacts and subsequent mortality associated with failed or incorrect identification may be reduced. A hyperspectral imager has been used to collect data in two water flow-through and one recirculating production site in parallel with the standard smoltification evaluations applied at these sites. The results from the latter have been used as baseline for a machine-learning algorithm trained to identify whether a fish was parr or smolt based on its spectral signature. The developed method correctly classified fish in 86% to 100% of the cases for individual sites, and had an overall average classification accuracy of 90%, thus indicating that analysis of spectral signatures may constitute a useful tool for smoltification monitoring.

Testing of novel net cleaning technologies for finfish aquaculture.

To avoid the negative impacts caused by biofouling development, aquaculture nets around the world are periodically cleaned using high-pressure washers. Net cleaning is labour-intense and costly, can damage antifouling coatings on the nets, and pose contamination as well as fish health and welfare risks. To support the environmental sustainability of the growing aquaculture sector, novel net cleaning methods are needed. This study examined low-pressure-, cavitation-, and suction-based cleaning technologies as alternatives to conventional high-pressure cleaning. Using field experiments, cleaning efficacy, cleaning waste generation, and the impact of cleaning on coating integrity and net strength were evaluated. Cavitation and high-pressure cleaning achieved considerably higher cleaning efficacy than low-pressure and suction cleaning. However, a single high-pressure treatment caused up to 53% coating degradation, compared to 2% for cavitation. All technologies produced similar cleaning waste and neither reduced net strength significantly. This study identifies cavitation cleaning as promising technology for biofouling control on aquaculture nets.

A simple method for weight estimation of whole herring (Clupea harengus) using planar X-ray imaging.

Fish weight is one of the key parameters used in commercial sorting of fresh fish. The capability of on-line and accurate estimation of the weight of each individual fish has a direct impact on profitability for the fish processors. Planar X-ray images of whole herring (n=16) were taken using a commercial medical X-ray apparatus. The integral intensity of all pixels in each image was calculated and correlated with the weight of the respective fish producing a linear relationship with a high correlation factor (R2=0.992) and a calculated mean prediction error of 2.5%. Planar X-ray imaging combined with simple image processing was successfully used to predict weight of whole herring. The technique has a potential to be used industrially for on-line weight estimation in sorting/grading systems.

High-speed weight estimation of whole herring (Clupea harengus) using 3D machine vision.

UNLABELLED: Weight is an important parameter by which the price of whole herring (Clupea harengus) is determined. Current mechanical weight graders are capable of a high throughput but have a relatively low accuracy. For this reason, there is a need for a more accurate high-speed weight estimation of whole herring. A 3-dimensional (3D) machine vision system was developed for high-speed weight estimation of whole herring. The system uses a 3D laser triangulation system above a conveyor belt moving at a speed of 1000 mm/s. Weight prediction models were developed for several feature sets, and a linear regression model using several 2-dimensional (2D) and 3D features enabled more accurate weight estimation than using 3D volume only. Using the combined 2D and 3D features, the root mean square error of cross-validation was 5.6 g, and the worst-case prediction error, evaluated by cross-validation, was ±14 g, for a sample (n = 179) of fresh whole herring. The proposed system has the potential to enable high-speed and accurate weight estimation of whole herring in the processing plants. PRACTICAL APPLICATION: The 3D machine vision system presented in this article enables high-speed and accurate weight estimation of whole herring, thus enabling an increase in profitability for the pelagic primary processors through a more accurate weight grading.