Tilapia Fish Weight Estimation Using Deep Learning and Computer Vision
Introduction
This study presents a 2D computer vision approach to estimate Tilapia weight in freshwater using a multi-camera system for video and image capturing.
Methodology
The method relies on a custom Tilapia image dataset including various fish growth stages, involving two main steps:
- Fish Detection: Using Mask R-CNN model for automatic Tilapia detection
- Weight Estimation: Calculating depth to convert pixel dimensions to centimeters, followed by weight prediction using regression models
Models Tested
Three regression models were tested for accuracy:
- Linear Regression
- Random Forest
- Support Vector Regression (SVR)
Advantages
- ✓ Cost-effective solution
- ✓ No need for expensive stereo cameras
- ✓ Non-intrusive, stress-free for fish
- ✓ High accuracy across growth stages
Grand Software design launched a smart fish farming management system for Tilapia in freshwater, combining AI and computer vision.
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