Smart Irrigation System

Sanskruti Gade , Nayan Bhingare , Aditi Lunavat

- Internet of Things (IoT), Smart Irrigation, Precision Agriculture, Machine Learning, Wireless Sensor Networks (WSN), Water Conservation, Cloud Computing.

The rapid depletion of global freshwater resources, coupled with the increasing demands of a growing population, necessitates a paradigm shift in agricultural practices. Traditional irrigation methods are notoriously inefficient, often leading to overwatering, soil degradation, and massive water wastage. This paper proposes a comprehensive Smart Irrigation System leveraging the Internet of Things (IoT) and Machine Learning (ML) algorithms to optimize water usage in precision agriculture. The proposed architecture integrates a network of low-cost sensors to monitor real-time environmental parameters, including soil moisture, temperature, humidity, and pH levels. Data collected by the sensor nodes is transmitted via wireless communication modules to a centralized cloud platform for processing. To enhance decision-making beyond simple threshold-based logic, the system employs predictive modeling using algorithms such as Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN). The integration of ML allows the system to forecast irrigation needs based on historical data and weather predictions, achieving predictive accuracies exceeding 95%. Simulated results and real-world deployment scenarios demonstrate that the proposed system can reduce water consumption by up to 40% compared to traditional timer-based systems, while simultaneously improving crop yield. Furthermore, this paper addresses current deployment challenges such as scalability, connectivity, and implementation costs, and explores future trends including 5G integration, Edge Computing, and Artificial Intelligence of Things (AIoT).

Volume 14 Issue 4, April-2026

Pages : f185-f188