https://github.com/nikh-iam/pest-classification-and-detection-system-using-deep-learning
https://github.com/nikh-iam/pest-classification-and-detection-system-using-deep-learning
css deeplearning flask html jupyter-notebook python
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/nikh-iam/pest-classification-and-detection-system-using-deep-learning
- Owner: nikh-iam
- Created: 2023-12-06T11:04:23.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-13T03:09:52.000Z (over 2 years ago)
- Last Synced: 2025-02-13T04:52:51.844Z (over 1 year ago)
- Topics: css, deeplearning, flask, html, jupyter-notebook, python
- Language: Jupyter Notebook
- Homepage:
- Size: 4.82 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Pest Classification and Detection System using Deep Learning
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.
## Key Features
✅ **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
## System Screenshots
### Prediction Interface
### Results Display
### Administrative Dashboard
## Technical Implementation
- **Deep Learning Model**: Custom-trained CNN for pest classification
- **Backend**: Python with Flask framework
- **Database**: MongoDB for pest information storage
- **Frontend**: Responsive web interface
## Installation Guide
### Prerequisites
- Python 3.8+
- MongoDB
- pip package manager
### Setup Instructions
1. **Download Required Files**:
- [Download model and dataset](https://drive.google.com/drive/folders/1Fb1J3y-kVtWnOmW-K21Bb-bIz6wlffdL?usp=sharing)
2. **Prepare Model Files**:
```bash
mkdir models
mv pest_model.pth models/
```
3. **Database Setup**:
```bash
# Create MongoDB database and collection
mongo
> use pest
> db.createCollection("pest_details")
# Import JSON data (use the downloaded file)
```
4. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
5. **Run the Application**:
```bash
python main.py
```
6. **Access the System**:
Open your browser and navigate to `http://localhost:5000`
## System Architecture
```
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Frontend UI │ ←→ │ Flask Server │ ←→ │ MongoDB │
│ │ │ │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
↑
│
┌─────────────────┐
│ │
│ Deep Learning │
│ Model │
│ │
└─────────────────┘
```
## Contributing
We welcome contributions! Please fork the repository and submit pull requests for any enhancements.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Contact
For questions or support, please contact on [gmail](mathewnikhil@gmail.com)