In urban environments, maintaining optimal plant health can be challenging due to varying environmental conditions and inconsistent care. GreenSage addresses these challenges by providing a sophisticated plant monitoring and care system that ensures plants receive the precise conditions they need to thrive.
| Plant | Status | Notes |
|---|---|---|
| Plant 1 | Dead | Soil moisture below optimal levels |
| Plant 2 | Healthy | Well-maintained environment |
| Plant 3 | Struggling | Needs adjustment in light exposure |
| Plant 4 | Dormant | Seasonal adaptation |
GreenSage leverages a diverse technology stack to ensure robust plant monitoring and care:
| Technology | Description |
|---|---|
| Golang | Backend development |
| Dart | Language for mobile app development |
| Flutter | UI framework for cross-platform apps |
| Arduino | Microcontroller for sensor integration |
| Moisture Sensor | Measures soil moisture levels |
| Temperature Sensor | Monitors ambient temperature |
| Humidity Sensor | Tracks humidity levels in the environment |
GreenSage provides predictions for plant health based on environmental data and machine learning algorithms:
| Plant | Predicted Health | Prediction Confidence |
|---|---|---|
| Plant 1 | Poor | 60% |
| Plant 2 | Excellent | 85% |
| Plant 3 | Fair | 70% |
| Plant 4 | Good | 75% |
- Implement machine learning algorithms for predictive plant care.
We welcome contributions from the community to enhance GreenSage's capabilities and usability.
This project is licensed under the MIT License - see the LICENSE.md file for details.