Precision Farming in Aquaculture: Use of a non-invasive, AI-powered real-time automated behavioural monitoring approach to predict gill health and improve welfare in Atlantic salmon (Salmo salar) aquaculture farms

Background:

When we are stressed, we sometimes bite our nails or bounce our legs. In Atlantic Salmon, stress can present itself as changes in feeding or swimming behavior. Shoaling is one example which refers to changes in swimming behavior leading multiple fish to group together. Shoaling, in addition to other changes in feeding and swimming behavior, can occur in response to stress from predators, changes in the environment, or health status.

In a recent study, Burke and collaborators have leveraged our current understanding of swimming and feeding behavioral changes to improve Atlantic Salmon farming practices. By installing video cameras in fish net pens (Fig. 1 of the preprint), the researchers could use Artificial Intelligence (AI) machine learning technology to recognize real-time patterns in fish behavior.

Detecting patterns in fish behavior indicative of stress and compromised health could provide farmers with early warning signs of stress, specifically gill disease in the case of this study. Gill disease is a major concern in salmon farming worldwide. Two types of gill disease, Amoebic Gill Disease (AGD) and Proliferative Gill Disease (PGD), are especially common and both of these cause breathing difficulties, hinder growth, and cause mortality. Unfortunately, with increasing sea temperatures, occurrences of AGD and PGD may also increase, highlighting the need for the early detection of fish stress to help prevent disease rather than treat symptoms.

Methods:

As part of this preprinted study, the researchers selected two fish farms (Farm A and Farm B) in Scotland and deployed video camera(s) in 9-10 cages per farm. Several behavioral characteristics were monitored including depth of fish activity (Depth of Max Activity) and proximity of the fish to one another (Activity). Specific Feeding Rate (SFR) were also calculated as ((feed given/fish biomass) X 100). Weekly gill health examinations were carried out where signs of AGD or PGD were scored on a scale of 0 (healthy tissue) – 5 (necrotic tissue) and mortality was monitored throughout the experiment.

Highlighted Key Findings:

1) Proliferative Gill Disease (PGD) was the main driver of increased activity levels

The researchers observed an increase in group fish activity at both farms, which coincided with poorer gill health status, specifically PDG (Fig. 2 of the preprint). On average, there was a 15% increase in activity at Farm A (which experienced poorer gill health), while there was a 7.7% increase at Farm B.

2) Increased fish activity indicates shoaling behavior

Increases in fish activity was concentrated towards the centre of the cage indicating fish shoaling in this area (Fig. 2 of the preprint). This behaviour is often a stress response, commonly triggered by the presence of predatory stimuli or environmental stressors

3) Mortality increased and Specific Feeding Rate (SFR) decreased after the onset of poor gill health

At both farms mortality increased with the onset of poor gill health (Fig. 3 of the preprint). SFR also increased with the onset of poor gill health though more prominently at Farm A where gill health was poorer than Farm B.

4) Group-level assessments are more informative than individual fish assessments

When behavioral changes began to emerge and gill health scores became worse, individual-level assessments of fish did not significantly change. Though individual assessments for signs of illness (changes in eyes, fins, skin) are important, by the time the outward signs of poor health emerge the fish may already be compromised. Thus, group assessments, such as behavioral change, are important for predicting impacts to fish.

Why I Highlight This Preprint:

This preprint presents an innovative solution to a globally relevant issue. The technology tested offers a practical, easily deployable device that farms can readily implement. Additionally, this technique has the potential to enhance salmon welfare, contributing to improved food security.

Questions for the Authors:

1. In comparison to individual fish assessments, approximately how much extra time could farmers gain by implementing this group-level behavioural assessment and what does this mean in terms of fish welfare?
2. Did the net pen with one strategically placed camera capture enough fish activity to serve as an effective early warning sign? Will farmers need to deploy more than one camera per net pen?

Tags: aquaculture, salmo salar

doi: https://doi.org/10.1242/prelights.38340

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