Precision Aquaculture

O’Donncha, Fearghal; Grant, Jon E.

doi:10.1109/iotm.0001.1900033

Cited by 56 publications

(31 citation statements)

References 9 publications

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“…Ranking predictors in this manner can quickly help sift through large datasets and understand data trends (Kuhn and Johnson, 2013). sister industry, agriculture, is in the need for sensing of the ambient environment also (e.g., water temperature, oxygen)-a consideration that is less important in agriculture where animals can be housed (O'Donncha and Grant, 2019). In this paper, we adopted acoustic measurements of fish distribution to quantify how environmental conditions influence and modify behaviour.…”

Section: Discussionmentioning

confidence: 99%

“…As farms are typically composed of 10-20 cages, and multiple farms are often co-located in a bay, the total number of individual fish is enormous. This precludes the direct translation of concepts from livestock farming, and in practise, precision aquaculture is a marriage of approaches developed for both precision livestock and grain cultivation, i.e., fish are not managed as individuals as are cows, yet are obviously more complex in management than plants (O'Donncha and Grant, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Data Driven Insight Into Fish Behaviour and Their Use for Precision Aquaculture

et al. 2021

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Aquaculture, or the farmed production of fish and shellfish, has grown rapidly, from supplying just 7% of fish for human consumption in 1974 to more than half in 2016. This rapid expansion has led to the growth of Precision Aquaculture concept that aims to exploit data-driven management of fish production, thereby improving the farmer's ability to monitor, control, and document biological processes in farms. Fundamental to this paradigm is monitoring of environmental and animal processes within a cage, and processing those data toward farm insight using models and analytics. This paper presents an analysis of environmental and fish behaviour datasets collected at three salmon farms in Norway, Scotland, and Canada. Information on fish behaviour were collected using hydroacoustic sensors that sampled the vertical distribution of fish in a cage at high spatial and temporal resolution, while a network of environmental sensors characterised local site conditions. We present an analysis of the hydroacoustic datasets using AutoML (or automatic machine learning) tools that enables developers with limited data science expertise to train high-quality models specific to the data at hand. We demonstrate how AutoML pipelines can be readily applied to aquaculture datasets to interrogate the data and quantify the primary features that explains data variance. Results demonstrate that variables such as temperature, wind conditions, and hour-of-day were important drivers of fish motion at all sites. Further, there were distinct differences in factors that influenced in-cage variations driven by local variables such as water depth and ambient environmental conditions (particularly dissolved oxygen). The framework offers a transferable approach to interrogate fish behaviour within farm systems, and quantify differences between sites.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Driven Insight Into Fish Behaviour and Their Use for Precision Aquaculture

et al. 2021

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“…This study is one of the few employing tags to resolve 3D positioning, but also able to incorporate high frequency data on both horizontal and vertical movement. As fish farming continues to embrace precision aquaculture (Føre et al, 2018a;O'Donncha and Grant, 2019), further delineation of both fish behaviour and its environmental forcing will be possible, as well as input into husbandry management.…”

Section: Discussionmentioning

confidence: 99%

Determining the Effects of Environmental Events on Cultured Atlantic Salmon Behaviour Using 3-Dimensional Acoustic Telemetry

Stockwell¹,

Filgueira²,

Grant³

2021

Front. Anim. Sci.

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The health and welfare of farmed fish are highly dependent on environmental conditions. Under suboptimal conditions, the negative impact on welfare can cause changes in fish behaviour. Acoustic tags can provide high resolution and high frequency data to monitor fish positioning within the cage, which can be used to infer swimming behaviour. In this study, implanted acoustic tags were used to monitor the three-dimensional positioning of Atlantic salmon (Salmo salar) at a commercial farm in Nova Scotia, Canada. The one-month study period allowed the characterisation of background behaviour and changes in behaviour in relation to different environmental conditions, namely, water characteristics in terms of dissolved oxygen and temperature caused by the fall overturn, storm conditions, and feeding activity. The three-dimensional position of 15 fish was recorded using high temporal resolution (3 s). Fish movement was characterised by calculating four fish variables: distance from the centre of the cage [m], depth [m], velocity [ms−1], and turning angle [°]. The population swam in a counterclockwise swimming direction around 4 ± 2 m depth at an average speed of 0.61 ± 0.38 ms−1. After the fall overturn, the population moved significantly towards cage centre while decreasing velocity, and non-significant differences in depth and turning angle were observed. During feeding periods, a significant increase in depth and velocity, as well as a reduction in turning angle were observed. The storm event did not cause any significant change in the four fish variables. While some of the behavioural changes were difficult to assess with respect to causation, the high resolution, high frequency data provide unique detailed positioning information to further our understanding of the swimming behaviour of farmed fish.

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“…Water safety is also an issue in food production. For example, water quality monitoring in aquaculture can be difficult due to labor intensity, high cost (of equipment and maintenance), and on-site application limitations [2]. Failure to detect deterioration of water quality can result in severe environmental changes, such as algal bloom, which can result in death of cultivated fish and consequential financial loss [3].…”

Section: Introductionmentioning

confidence: 99%

An Energy-Autonomous Chemical Oxygen Demand Sensor Using a Microbial Fuel Cell and Embedded Machine Learning

2021

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The current methods of water quality monitoring tend to be costly, labor-intensive, and offsite. Also, they are not energetically sustainable and often require environmentally damaging power sources such as batteries. Microbial fuel cell (MFC) technology is a promising sustainable alternative to combat these issues due to its low cost, eco-friendly energy generation, and bio-sensing features. Extensive work has been done on using MFCs as bio-sensors or sources of power separately. However, little work has been done toward using MFCs for both applications at the same time. Additionally, previous studies using MFCs for water quality measurement have been mostly limited to laboratory conditions due to the biochemical complexity of the real-world. Another limitation of MFCs is how little power they can generate, requiring the MFC-based systems to have minimal power consumption. This work addresses these challenges and presents an energy-autonomous water quality sensing unit that uses a single MFC both as its sensory input and the sole source of power for computing the chemical oxygen demand (COD). In the proposed unit, geometric features of the voltage profile of the MFC (e.g., peak heights) are used as the inputs to a machine learning algorithm (support vector regression (SVR)). The electrical power generated by the MFC is used to drive a low-power microcontroller which logs the MFC voltage and runs the machine learning algorithm. Experimental evaluation showed that the device is capable of detecting the COD of a natural pond water samples accurately (coefficient of determination (R 2 ) = 0.94). The work is the first demonstration of energy autonomy in an MFC-based sensing unit for measuring water quality and represents a step forward in the development of energy-autonomous sensors for environmental monitoring applications.INDEX TERMS Microbial fuel cells, bio-sensing, energy autonomy, machine learning.

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Precision Aquaculture

Cited by 56 publications

References 9 publications

Data Driven Insight Into Fish Behaviour and Their Use for Precision Aquaculture

Data Driven Insight Into Fish Behaviour and Their Use for Precision Aquaculture

Determining the Effects of Environmental Events on Cultured Atlantic Salmon Behaviour Using 3-Dimensional Acoustic Telemetry

An Energy-Autonomous Chemical Oxygen Demand Sensor Using a Microbial Fuel Cell and Embedded Machine Learning

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