Optimization of Irrigation Scheduling Based on Soil Moisture and Weather Prediction Data

Abstract

This project focuses on improving irrigation efficiency through a smart irrigation system that combines real-time soil moisture sensing with weather forecasting. By adjusting watering based on soil conditions and climate predictions, the system reduces water wastage, prevents over-irrigation, and supports better crop health. It uses soil moisture sensors and temperature–humidity modules and includes soil testing for physical, chemical, and biological properties. The study emphasizes NPK fertilizer use and is tested on four major crops: sugarcane, cotton, oranges, and grapes. Designed for small-scale farmers and educational use, the system is low-cost and does not rely on wireless networks. Results showed improved water efficiency and crop performance compared to traditional methods. This approach promotes sustainable agriculture and intelligent resource management.