## Overview # Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia Feeding This repository contains a YOLOv8-based model for precise Tilapia feeding in aquaculture, combining computer vision and IoT technologies. Our system uses real-time IoT sensors to monitor water quality and computer vision to analyze fish size and count, determining optimal feed amounts. We achieved 94% precision in keypoint detection on a dataset of 3,500 annotated Tilapia images, enabling accurate weight estimation from fish length. The system includes a mobile app for remote monitoring and control. Our approach significantly improves aquaculture efficiency, with preliminary estimates suggesting a potential increase in production of up to 58 times compared to traditional farming methods. This repository includes our trained models, code, and a curated open-source dataset of annotated Tilapia images. ## How to use Please download the model weights first [Counting Model](https://huggingface.co/Raniahossam33/Fish-Counting/blob/main/Fish-Counting-yolov8.pt) [Keypoint Detection Model](https://huggingface.co/Raniahossam33/Fish-Counting/blob/main/KeyPoint-Detction-Yolov8.pt) [Paper](https://arxiv.org/abs/2409.08695) ```python from ultralytics import YOLO from PIL import Image img = Image.open('') model = YOLO('') results = model(img) ``` ## Results

## Applications This fish counting model can be useful in various scenarios, including: - Monitoring fish populations in aquariums or fish farms - Ecological studies in natural water bodies - Automated fish stock assessment ## Citation If you use this model in your research, please cite: ```bibtex @article{hossam2024precision, title={Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia Feeding}, author={Hossam, Rania and Heakl, Ahmed and Gomaa, Walid}, journal={arXiv preprint arXiv:2409.08695}, year={2024}, doi={10.48550/arXiv.2409.08695} } ```