An Innovative Smart Irrigation Using Embedded and Regression-Based Machine Learning Technologies for Improving Water Security and Sustainability

This innovative smart irrigation system leverages embedded technologies and regression-based machine learning algorithms to enhance water security and promote sustainable agricultural practices. At its core, a Raspberry Pi serves as the central processing unit, interfacing with a suite of sensors—including soil moisture, pH, and DHT11 environmental sensors—to continuously monitor soil and atmospheric conditions. Data collected through these sensors is processed, with regression models predicting optimal irrigation schedules to minimize water wastage while maximizing crop health. A USB web camera provides visual monitoring, and an LCD display offers user-friendly feedback. The system controls dual water pumping motors via relays, actuated according to sensor inputs and machine learning predictions. An ADC module ensures accurate sensor readings, while a reliable 12V power supply and memory card support uninterrupted operation and data storage. This integrated approach not only automates irrigation but also supports intelligent decision-making, ultimately fostering sustainable water use in agriculture.

Keywords: Smart Irrigation System, Embedded Systems, Raspberry Pi, Machine Learning,

Regression Algorithms, Water Security, Sustainable Agriculture, Soil Moisture Sensor, pH Sensor,  DHT11 Sensor, ADC Module, Water Pump Control