Intelligent Aquaculture Monitoring Systems

Intelligent aquaculture monitoring systems integrate advanced sensor networks, data analytics and automated control to enhance the sustainability, productivity and welfare of aquatic farming operations. By combining environmental sensors (for temperature, dissolved oxygen, pH and turbidity) with computer vision, machine‐learning algorithms and Internet of Things (IoT) connectivity, these platforms deliver real‐time insights into stock density, feeding efficiency and health status. Such systems may further incorporate energy‐autonomous designs—using solar or photovoltaic/battery hybrids—to enable continuous off‐grid operation. The convergence of robotics, unmanned vehicles and narrowband IoT enables unprecedented spatial coverage, from nearshore tanks to offshore cages. Practical applications span biomass estimation without invasive sampling, precision feed delivery based on individual behaviour recognition, disease outbreak early warning via trajectory analysis, and holistic water quality assessment through composite metrics. By facilitating data‐driven decisions, these intelligent frameworks promise to reduce environmental impact, increase economic returns and ensure high standards of animal welfare on a global scale.

Research from Nature Portfolio

Recent studies have focused on high‐accuracy animal tracking under challenging conditions, using deep‐learning models to resolve long‐standing issues in behaviour analysis. In foundational work on zebrafish, convolutional neural networks were trained to distinguish individual fish even after occlusion, achieving highly accurate trajectory reconstruction over extended periods. This approach not only enabled robust quantification of movement patterns and social interactions but also demonstrated that machine‐vision systems can maintain identity persistence in densely populated environments. The outcomes underscore the feasibility of deploying similar deep‐learning frameworks for commercially important species, laying the groundwork for scalable, automated behaviour recognition modules in precision aquaculture monitoring.

Intelligent Aquaculture Monitoring Systems publication trend

The graph below shows the total number of articles in intelligent aquaculture monitoring systems across all publications each year (not limited to Nature Index journals).

Technical terms

Internet of Things (IoT): A network of interconnected sensors and devices that collect and exchange data in real time.

Convolutional Neural Network (CNN): A class of deep‐learning model specialised in processing grid‐structured data, such as images, for feature extraction and classification.

Environmental DNA (eDNA): Genetic material shed by organisms into their environment, used as a noninvasive biomarker to estimate species abundance.

Narrowband IoT (NB-IoT): A low-power wide-area network protocol optimised for transmitting small amounts of data over long distances in IoT applications.

Unmanned Aerial Vehicle (UAV): An aircraft operated without a human pilot onboard, used here as a mobile platform for remote sensing and data acquisition.

Precision Fish Farming (PFF): A control-engineering framework that applies sensor technology, automation and data analytics to optimise aquaculture processes.

Water Quality Suitability Index (WQSI): A composite metric derived from multiple water-quality parameters to indicate the overall health of an aquatic environment.

References

1. Precision fish farming: A new framework to improve production in aquaculture. Biosystems Engineering (2018).
2. Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review. Reviews in Aquaculture (2019).
3. Zebrafish tracking using convolutional neural networks. Scientific Reports (2017).
4. A standalone photovoltaic/battery energy-powered water quality monitoring system based on narrowband internet of things for aquaculture: Design and implementation. Smart Agricultural Technology (2023).
5. Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision. Electronics (2021).
6. A Review of Unmanned System Technologies with Its Application to Aquaculture Farm Monitoring and Management. Drones (2022).