Description:

The Crop Yield Prediction System is a data science–driven platform that helps farmers, agricultural researchers, and government agencies forecast the yield of various crops based on historical agricultural data, soil conditions, climate patterns, and farming practices.
By combining machine learning models with real-time weather and soil data, the system enables better crop planning, resource allocation, and food supply chain management.

This project is particularly useful for precision agriculture, food security monitoring, and agricultural policy-making.

Key Features:

1. Historical Crop Data Analysis – Uses past yield records to identify growth trends.

2. Soil Parameter Integration – Considers soil pH, nitrogen, phosphorus, potassium levels, and organic matter.

3. Weather Impact Modeling – Factors in rainfall, temperature, humidity, and sunshine hours.

4. Crop Variety Recommendations – Suggests the best crop types for current conditions.

5. Yield Prediction – Uses regression or neural network models to forecast expected production.

6. Real-Time Data Updates – Connects to weather APIs and IoT soil sensors for live data.

7. Interactive Dashboard – Displays predictions, graphs, and regional yield comparisons.

8. Risk Assessment – Identifies potential threats like drought, pests, or floods.

Technology Stack:

• Backend: Node.js / Java / PHP (for API development and data processing)

• Frontend: HTML, CSS, Bootstrap, JavaScript (Chart.js / D3.js for visualizations)

• Database: MySQL / PostgreSQL / MongoDB (stores crop records, soil data, weather history)

• Data Science Layer: Python (pandas, NumPy, scikit-learn, TensorFlow for ML models)

• APIs: OpenWeatherMap API, FAO crop data, SoilGrids API

• IoT Integration (Optional): Soil moisture, temperature, and nutrient sensors

Example Use Case:

• A farmer in Punjab wants to know if wheat will have good yield this season.

• The system collects soil data (pH 6.5, nitrogen-rich), weather forecast (moderate rainfall, 18–25°C), and past yield data.

• The prediction model forecasts 2.8 tons per hectare, a 15% increase from last year.

• The dashboard also suggests using a specific wheat variety that performs better in slightly cooler weather.