Abstract:
The increasing demand for efficient water management in agriculture has led to the development of intelligent irrigation systems. This project presents a Real-Time Smart Irrigation System powered by Machine Learning (ML) and IoT, aimed at optimizing water usage and enhancing crop productivity. The system integrates Arduino Uno for sensor data acquisition and NodeMCU (ESP8266) for real-time data transmission to a cloud-based server.
Key environmental parameters such as soil moisture, temperature, and soil pH are continuously monitored using dedicated sensors. The data is collected by the Arduino Uno and transmitted via NodeMCU to an online platform for processing. A lightweight machine learning model, trained using historical sensor data and weather patterns, predicts the optimal irrigation schedule and water quantity.
Based on the ML model's output, the system autonomously controls a water pump, ensuring that irrigation occurs only when necessary. The real-time data, system status, and irrigation decisions are accessible via a web or mobile dashboard for remote monitoring and control.
This approach ensures resource-efficient irrigation, reduces human intervention, and supports sustainable agriculture by adapting dynamically to changing environmental conditions.
Introduction:
Agriculture is the backbone of many economies, yet traditional irrigation methods often lead to excessive water usage, reduced crop yields, and poor soil health. With growing concerns over water scarcity and environmental sustainability, it has become essential to adopt intelligent solutions that can optimize water usage without compromising agricultural productivity.
Smart irrigation systems use modern technologies like Internet of Things (IoT) and Machine Learning (ML) to make data-driven decisions for watering crops. In this project, we propose a real-time smart irrigation system that leverages sensor data and ML algorithms to monitor soil and environmental conditions and control irrigation accordingly.


The system uses an Arduino Uno to interface with multiple sensors, including:
• A soil moisture sensor to detect the water level in the soil,
• A temperature sensor (e.g., DHT11 or LM35) to monitor ambient conditions,
• A pH sensor to assess soil acidity or alkalinity.
The data collected is sent to a NodeMCU (ESP8266), which connects to the internet and uploads the readings to a cloud server. A lightweight machine learning model processes the real-time data to predict irrigation needs based on current and historical trends.
By automatically controlling a water pump based on intelligent decisions, this system:
• Reduces water wastage,
• Improves crop health, and
• Minimizes manual effort.


Objectives:
• To build a smart irrigation system using Arduino and NodeMCU.
• To check soil moisture, temperature, and pH using sensors.
• To send the sensor data to the cloud using Wi-Fi.
• To use machine learning to decide when to water the plants.
• To turn the water pump on or off automatically.
• To save water and improve plant growth.
• To help farmers monitor the system from anywhere.