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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.
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## How to use
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Please download the model weights first
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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.
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## How to use
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Please download the model weights first
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