FarmGaze is an intelligent machine learning–based platform designed to support precision farming through real-time analytics, crop recommendations, optimized irrigation, and environmental monitoring. It aims to improve productivity, conserve water, and assist farmers in making data-driven decisions.
This system integrates multiple ML models, weather forecasting, geospatial mapping, and interactive visualizations to build a smarter irrigation framework tailored for precision agriculture.
Recommends the most suitable crop for cultivation based on:
- Soil nutrients (N, P, K)
- pH and organic content
- Regional climate patterns
- Historical crop performance
Automates irrigation planning using:
- Soil moisture levels
- Crop type and water requirements
- Local weather forecasts
Reduces water consumption by:
- Predicting optimal irrigation volume
- Learning from usage history
- Adapting to seasonal and crop-specific variations
Forecasts water levels in storage units such as:
- Farm tanks
- Reservoirs
- Canals
Using rainfall, consumption, and environmental data
Evaluates soil quality through:
- Nutrient analysis (NPK)
- pH value, moisture content, and EC
- Classification for crop suitability
Provides real-time and predictive weather data essential for:
- Irrigation decisions
- Pest/disease risk analysis
- Seasonal crop planning
Key data points include:
- Temperature
- Humidity
- Rainfall probability
- Wind speed
- Solar radiation
Includes geospatial visualizations for:
- Soil and crop distribution
- Water-stressed regions
- Vegetation health (NDVI)
- Weather overlay on maps
A user-friendly dashboard that displays:
- Model predictions and results
- Crop growth trends
- Irrigation schedules
- Historical analytics
- Resource usage summaries
- Languages: Python, JavaScript
- Machine Learning: scikit-learn, XGBoost, TensorFlow
- Web Frameworks: Flask or FastAPI
- Frontend: HTML, CSS, JavaScript (optionally React)
- Visualization: Plotly, Dash, Chart.js
- Geo Mapping: Leaflet.js, Google Maps API, GeoPandas
- Database: SQLite, Firebase, or MongoDB
models/– All ML models (crop predictor, irrigation, etc.)weather/– Weather forecast integrationmaps/– Geospatial mapping toolsvisualizations/– Dashboards and chartstemplates/– HTML frontend pagesstatic/– Static assets (CSS, JS)app.py– Main backend application
- IoT integration with real-time sensors
- SMS or app-based farmer notifications
- Mobile-friendly interface
- Multilingual support for regional accessibility
- More advanced predictive models using deep learning
Built with the mission to empower farmers, reduce resource wastage, and drive the future of sustainable agriculture through AI and data.