Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia Feeding
This addresses productivity and environmental issues in aquaculture, but it is an incremental application of existing technologies to a specific domain.
The paper tackles inefficient feeding in fish farming by developing a system that combines computer vision and IoT to optimize Tilapia feeding, achieving 94% precision in weight estimation and potentially increasing production up to 58 times compared to traditional methods.
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.