The shrimp_food_detection project aims to help with feeding efficiency in shrimp farming by automating the detection and visualization of leftover food in shrimp ponds using advanced computer vision techniques. Additionally, it incorporates a Convolutional Neural Network (CNN) model to detect the presence of shrimp, enabling shrimp farmers to adjust feeding schedules more precisely and monitor shrimp activity effectively. This project presents a novel approach to enhancing sustainability and profitability in shrimp farming operations.
- Food Detection: Utilizes the Harris corner detection algorithm enhanced with non-maximum suppression to identify potential leftover food particles.
- Shrimp Detection: Employs a CNN model, specifically the ResNet architecture, to detect the presence of shrimp within the pond environment.
- Real-time Visualization: Offers real-time graphs to visualize the quantity of leftover food and shrimp activity, aiding in the optimization of feeding schedules.
- Pond Detection: Employs a 3 layer encoder and a 1 layer decoder CNN model to detect the pond, accurate against different shapes.
To set up the shrimp_food_detection project, follow these steps:
- Clone the repository:
git clone https://github.com/yourrepository/shrimp_food_detection.git
- Navigate to the project directory:
cd shrimp_food_detection
- Install the required dependencies:
pip install -r requirements.txt
- To run the application, execute the following command in the terminal:
python app.py
- For training the shrimp detection model, run:
jupyter notebook train.ipynb
- For pond identification, run:
jupyter notebook pond_identify.ipynb
Contributions to shrimp_food_detection are welcome!
Distributed under the MIT License. See LICENSE for more information.