Summary of Precision Aquaculture: An Integrated Computer Vision and Iot Approach For Optimized Tilapia Feeding, by Rania Hossam et al.
Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia Feeding
by Rania Hossam, Ahmed Heakl, Walid Gomaa
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This innovative system for precision feeding in fish farming combines computer vision and IoT technologies. The system uses real-time sensors to monitor water quality and computer algorithms to analyze fish size and count, determining optimal feed amounts. A mobile app enables remote monitoring and control. The solution utilizes YOLOv8 for keypoint detection to measure Tilapia weight from length, achieving 94% precision on 3,500 annotated images. The method is designed to significantly improve results in traditional farms, with preliminary estimates suggesting a potential increase of up to 58 times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This system helps fish farming by using technology to make sure the fish get just the right amount of food. It uses special sensors and cameras to figure out how big the fish are and how much food they need. This helps keep the water clean and makes the farm more productive. The app lets farmers check in on their fish from anywhere. The system is really good at measuring the fish’s size, getting it right 94% of the time. |
Keywords
» Artificial intelligence » Precision
