🌱 Smart Irrigation System – AI Powered

An intelligent irrigation management solution that predicts watering needs based on real-time environmental data using Machine Learning and a Streamlit web interface.

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📌 Overview

The Smart Irrigation System is an AI-driven project designed to support farmers and gardeners by automating irrigation decisions. By analyzing soil moisture, humidity, and temperature values, the system predicts whether irrigation is required — helping save water and improve crop health.

The project includes:

• A trained ML model
• A responsive Streamlit app
• Data visualizations
• Full deployment on Streamlit Cloud

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🚀 Live Demo

🌐 Streamlit App: https://smartirrigation-vkwxvp488mdduptgmjrhbt.streamlit.app/

📂 GitHub Repository: https://github.com/Chandrika987/Smart_Irrigation

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🧠 Features

• ✔ AI-Based Irrigation Prediction
• ✔ Sensor-Based Inputs (Humidity, Moisture, Temperature)
• ✔ Responsive Streamlit Web Application
• ✔ Easy-to-Use Mobile-Friendly UI
• ✔ Real-Time Decision Dashboard
• ✔ Pump & Sprinkler Activity Visualization
• ✔ Fully Deployed Online

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🏗️ Tech Stack

🌐 Frontend

• Streamlit (UI development)
• Custom CSS (Responsive styling)

🤖 Machine Learning

• Scikit-Learn – Model training & prediction
• Joblib – Model exporting and loading
• NumPy – Numerical computations
• Pandas – Data preprocessing & analysis

📊 Visualization

• Matplotlib – Data visualizations
• Streamlit built-in charts

📁 Backend / Deployment

• Python 3.x
• Streamlit Cloud (App hosting & deployment)

🗂️ Tools Used

• Git & GitHub (version control + hosting)
• Jupyter Notebook (smart_irrigation.ipynb)
• VS Code / IDE

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📊 Model Details

The ML model predicts irrigation need (ON/OFF) using:

• Soil Moisture (%)
• Temperature (°C)
• Humidity (%)

✔ ML Workflow

1. Data Cleaning
2. Data Preprocessing
3. Feature Selection
4. Train-Test Split
5. Model Training
6. Model Evaluation
7. Saving Model as .pkl
8. Integrating Model with Streamlit App

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📁 Project Structure

Smart_Irrigation/
│── Smart_Sprinkler_app.py
│── Farm_Irrigation_system.pkl
│── irrigation_machine.csv
│── pump_activity.png
│── smart_irrigation.ipynb
│── requirements.txt
└── README.md

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🖥️ Run the Project Locally

1️⃣ Clone the Repository

git clone https://github.com/Chandrika987/Smart_Irrigation.git
cd Smart_Irrigation

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run the Streamlit App

streamlit run Smart_Sprinkler_app.py

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📱 Mobile Optimization

The UI is custom-designed to ensure:

• Readable text
• Proper spacing
• Touch-friendly sliders
• Clean layout even on small screens

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🧩 Future Improvements

• 🌦️ Weather API integration
• 🌾 Multi-parcel irrigation scheduling
• 📡 IoT sensor & hardware integration
• 🧪 Fertilizer recommendation model
• 🔔 Alerts via SMS/WhatsApp

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👩‍💻 Author

Chandrika Pala 🌍 India

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⭐ If you like this project, please give it a star on GitHub!

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