Smart Irrigation Prediction System
Smart Irrigation Prediction System
AI-Based Irrigation Optimization Using Weather & Soil Data
Problem Statement
Traditional irrigation methods often lead to overwatering or underwatering due to lack of real-time data and prediction. This results in water wastage, poor crop growth, and increased farming costs.
Solution Overview
The Smart Irrigation Prediction System uses machine learning models combined with soil moisture sensor data and real-time weather forecasts to predict the optimal irrigation time and water quantity for crops.
Key Features
Farmer Features
- • Automated irrigation recommendations
- • Real-time soil moisture monitoring
- • Weather-based predictions
- • Water usage optimization
Admin Features
- • Sensor & device management
- • Crop and soil configuration
- • Historical data analysis
- • Alert & notification settings
System Architecture
Soil Moisture Sensors
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IoT Device (Raspberry Pi / MCU)
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Weather API Integration
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ML Prediction Model
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Irrigation Decision Engine
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Dashboard / Alerts
Technology Stack
IoT & Sensors
Soil Moisture Sensors, Raspberry Pi / Microcontrollers
Machine Learning
Python, Scikit-learn, Time-series Prediction Models
Weather Data
OpenWeather APIs, Real-time Forecast Data
Backend
Python, FastAPI, REST APIs
Frontend
Web Dashboard, Tailwind CSS
Use Cases
- • Precision agriculture
- • Smart farming solutions
- • Water resource optimization
- • Greenhouse management
- • Crop yield improvement
- • Sustainable farming initiatives
Business Impact
The Smart Irrigation Prediction System helps reduce water consumption, improve crop yield, and lower operational costs. By enabling data-driven irrigation decisions, it supports sustainable agriculture practices and delivers measurable efficiency gains for farmers and agribusinesses.