Precision fish farming: a new framework to improve production in aquaculture

Aquaculture production of finfish has seen rapid growth in production volume and economic yield over the last decades, and is today a key provider of seafood. As the scale of production increases, so does the likelihood that the industry will face emerging biological, economic and social challenges that may influence the ability to maintain ethically sound, productive and environmentally friendly production of fish. It is therefore important that the industry aspires to monitor and control the effects of these challenges to avoid also upscaling potential problems when upscaling production. We introduce the Precision Fish Farming (PFF) concept whose aim is to apply control-engineering principles to fish production, thereby improving the farmer's ability to monitor, control and document biological processes in fish farms. By adapting several core principles from Precision Livestock Farming (PLF), and accounting for the boundary conditions and possibilities that are particular to farming operations in the aquatic environment, PFF will contribute to moving commercial aquaculture from the traditional experience-based to a knowledge-based production regime. This can only be achieved through increased use of emerging technologies and automated systems. We have also reviewed existing technological solutions that could represent important components in future PFF applications. To illustrate the potential of such applications, we have defined four case studies aimed at solving specific challenges related to biomass monitoring, control of feed delivery, parasite monitoring and management of crowding operations

The New Era of Physio-Logging and Their Grand Challenges.

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Behavioural indicators of welfare in farmed fish

Behaviour represents a reaction to the environment as fish perceive it and is therefore a key element of fish welfare. This review summarises the main findings on how behavioural changes have been used to assess welfare in farmed fish, using both functional and feeling-based approaches. Changes in foraging behaviour, ventilatory activity, aggression, individual and group swimming behaviour, stereotypic and abnormal behaviour have been linked with acute and chronic stressors in aquaculture and can therefore be regarded as likely indicators of poor welfare. On the contrary, measurements of exploratory behaviour, feed anticipatory activity and reward-related operant behaviour are beginning to be considered as indicators of positive emotions and welfare in fish. Despite the lack of scientific agreement about the existence of sentience in fish, the possibility that they are capable of both positive and negative emotions may contribute to the development of new strategies (e. g. environmental enrichment) to promote good welfare. Numerous studies that use behavioural indicators of welfare show that behavioural changes can be interpreted as either good or poor welfare depending on the fish species. It is therefore essential to understand the species-specific biology before drawing any conclusions in relation to welfare. In addition, different individuals within the same species may exhibit divergent coping strategies towards stressors, and what is tolerated by some individuals may be detrimental to others. Therefore, the assessment of welfare in a few individuals may not represent the average welfare of a group and vice versa. This underlines the need to develop on-farm, operational behavioural welfare indicators that can be easily used to assess not only the individual welfare but also the welfare of the whole group (e. g. spatial distribution). With the ongoing development of video technology and image processing, the on-farm surveillance of behaviour may in the near future represent a low-cost, noninvasive tool to assess the welfare of farmed fish.Fundação para a Ciência e Tecnologia, Portugal [SFRH/BPD/42015/2007]info:eu-repo/semantics/publishedVersio

Bio-sensing technologies in aquaculture: how remote monitoring can bring us closer to our farm animals

Farmed aquatic animals represent an increasingly important source of food for a growing human population. However, the aquaculture industry faces several challenges with regard to producing a profitable, ethical and environmentally sustainable product, which are exacerbated by the ongoing intensification of operations and increasingly extreme and unpredictable climate conditions. Fortunately, bio-sensors capable of measuring a range of environmental, behavioural and physiological variables (e.g. temperature, dissolved gases, depth, acceleration, ventilation, heart rate, blood flow, glucose and l-lactic acid) represent exciting and innovative tools for assessing the health and welfare of farmed animals in aquaculture. Here, we illustrate how these state-of-the-art technologies can provide unique insights into variables pertaining to the inner workings of the animal to elucidate animal–environment interactions throughout the production cycle, as well as to provide insights on how farmed animals perceive and respond to environmental and anthropogenic perturbations. Using examples based on current challenges (i.e. sub-optimal feeding strategies, sub-optimal animal welfare and environmental changes), we discuss how bio-sensors can contribute towards optimizing the growth, health and welfare of farmed animals under dynamically changing on-farm conditions. While bio-sensors currently represent tools that are primarily used for research, the continuing development and refinement of these technologies may eventually allow farmers to use real-time environmental and physiological data from their stock as ‘early warning systems\u27 and/or for refining day-to-day operations to ethically and sustainably optimize production. This article is part of the theme issue ‘Measuring physiology in free-living animals (Part I)’

Monitoring and feeding integration of demand feeder systems

This chapter highlights the findings of the developmental monitoring systems for swimming pattern or motion analysis with regard to feeding behaviour. A benchmark for examining the framework on how scientists control fish in animal variable function factors was gathered and referred to gauge the adequate design in constructing a viable device. The validation of image processing and automated demand feeder to determine the results will also be considered, as a validation aspect between the system of tracking and the behaviour of the Lates calcarifer where the pixel intensity will be extracted as the features. The results of this chapter will enable the reader on the development of an integrated feeder scheme that consolidates surveillance scheme to identify the feeding behaviour and relation towards the specific growth rate (SGR)