IoT Crop Yield Prediction System

Combining IoT sensors with machine learning enables data-driven farming decisions. A Crop Yield Prediction System collects environmental data throughout the growing season and uses ML models to predict harvest outcomes, helping farmers plan sales and resources.

Why This Project Is Popular

Precision agriculture is the future of farming. This project combines hardware (sensors) with software (ML), demonstrating full-stack IoT skills. Predictive analytics in agriculture is a hot research area.

Project Ideas or Guide

Build a yield prediction system:

• Data Collection: Continuous monitoring of temperature, humidity, rainfall, soil moisture, sunlight.
• Weather API: Fetch historical and forecast weather data.
• Cloud Storage: Store data in Firebase or cloud database.
• ML Model: Train regression model (Python/TensorFlow) on historical data.
• Prediction API: Serve model predictions via backend API.
• Dashboard: Show predicted yield vs actual to farmers.

Components Used

• ESP32 (multiple units for field coverage)
• DHT22, Rain Sensor
• Soil Sensors
• Light Sensor (BH1750)
• Cloud Database
• Python Backend

FAQ

Q: How accurate are predictions?

A: Accuracy depends on data quality and model training - typically 70-85%.

Q: Can this work for multiple crops?

A: Yes, train separate models for each crop type.