Pests significantly affect agricultural yields, leading to declines in productivity and nutrient depletion. Excessive pesticide usage often results in increased pesticide residues, disrupting the food chain and causing adverse effects on human health and the environment. Our deep learning-based solution automates pest detection and classification to address these challenges.
✅ Automated Pest Detection - Advanced deep learning model for accurate pest identification
⚡ Efficient Processing - Rapid image analysis outperforming manual inspection
🎯 High Accuracy - State-of-the-art classification performance
🌐 Scalable Solution - Ready for large-scale agricultural deployment
🖥️ User-Friendly Interface - Accessible to non-technical users
📚 Comprehensive Pest Database - Detailed information including descriptions and treatment recommendations
- Deep Learning Model: Custom-trained CNN for pest classification
- Backend: Python with Flask framework
- Database: MongoDB for pest information storage
- Frontend: Responsive web interface
- Python 3.8+
- MongoDB
- pip package manager
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Download Required Files:
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Prepare Model Files:
mkdir models mv pest_model.pth models/
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Database Setup:
# Create MongoDB database and collection mongo > use pest > db.createCollection("pest_details") # Import JSON data (use the downloaded file)
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Install Dependencies:
pip install -r requirements.txt
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Run the Application:
python main.py
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Access the System: Open your browser and navigate to
http://localhost:5000
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Frontend UI │ ←→ │ Flask Server │ ←→ │ MongoDB │
│ │ │ │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
↑
│
┌─────────────────┐
│ │
│ Deep Learning │
│ Model │
│ │
└─────────────────┘
We welcome contributions! Please fork the repository and submit pull requests for any enhancements.
This project is licensed under the MIT License - see the LICENSE file for details.
For questions or support, please contact on gmail