Goh, Ken How (2025) Exploring the potential of using arUco markers to monitor fish feeding status. Final Year Project, UTAR.
Abstract
Efficient feeding management is a cornerstone of sustainable aquaculture, directly influencing fish growth, health, and resource utilization. Traditional feeding methods, which rely on manual observation to determine satiety, are labour-intensive, subjective, and prone to human error—often resulting in overfeeding and operational inefficiencies. This project presents a novel approach for monitoring fish feeding status by leveraging ArUco marker tracking. Pose estimations of floating markers are analysed to extract movement intensity, which is then interpreted using a time-series LSTM classification model to detect fish activity and infer satiety levels. The system was developed using a combination of Python, Keras, and OpenCV, and deployed in a real aquaculture setting using red hybrid tilapia (Oreochromis sp.). A web-based interface provides real-time pose data, fish activity classification, feeding recommendations, and status tracking. Model performance was validated through cross-validation and real-world testing, achieving high accuracy and practical reliability. Beyond monitoring fish feeding status, the system also detects air pump operation and tracks water level variations, offering a broader view of tank conditions. It supports multi-tank monitoring using a single camera, making the solution cost-effective, scalable, and non-invasive. The results affirm the system’s potential to improve feed management, reduce labour dependency, and support more intelligent and sustainable aquaculture practices.
Actions (login required)
![[img]](http://eprints.utar.edu.my/style/images/fileicons/application_pdf.png)
