Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia Feeding

TL;DR

This paper presents an integrated computer vision and IoT system for precise Tilapia feeding, improving efficiency and environmental sustainability in aquaculture through real-time monitoring and advanced image analysis.

Contribution

It introduces a novel system combining IoT sensors and YOLOv8-based computer vision for accurate fish size measurement and optimized feeding in aquaculture.

Findings

94% accuracy in fish weight estimation from images

Potential to increase production up to 58 times

Open-source models and dataset available

Abstract

Traditional fish farming practices often lead to inefficient feeding, resulting in environmental issues and reduced productivity. We developed an innovative system combining computer vision and IoT technologies for precise Tilapia feeding. Our solution uses real-time IoT sensors to monitor water quality parameters and computer vision algorithms to analyze fish size and count, determining optimal feed amounts. A mobile app enables remote monitoring and control. We utilized YOLOv8 for keypoint detection to measure Tilapia weight from length, achieving \textbf{94\%} precision on 3,500 annotated images. Pixel-based measurements were converted to centimeters using depth estimation for accurate feeding calculations. Our method, with data collection mirroring inference conditions, significantly improved results. Preliminary estimates suggest this approach could increase production up to 58 times compared to traditional farms. Our models, code, and dataset are open-source~\footnote{The code, dataset, and models are available upon reasonable request.