ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: TRANSFORMING TEACHING, LEARNING, AND STUDENT ENGAGEMENT
 https://doi.org/10.65725/IJHLT/1/2/001

INTERNATIONAL JOURNAL OF HUMANITIES AND LEARNING TECHNOLOGY INNOVATION (IJHLT)
Volume 1, Issue 2, July – September 2026

Abstract:
Efficient irrigation management is essential for sustainable agriculture, particularly in the context of increasing freshwater scarcity and the growing imperative to optimize crop productivity. Conventional irrigation practices rely predominantly on fixed time schedules or manual field assessments, which frequently induce over-irrigation, root-zone nutrient leaching, under-irrigation water stress, and substantial resource inefficiency. This paper proposes an Intelligent Irrigation Management System that integrates Internet of Things (IoT) sensing architectures, multi-parameter environmental telemetry, and supervised machine learning (ML) algorithms to facilitate dynamic, data-driven, and automated irrigation control. The proposed system continuously acquires real-time field data—including soil moisture, ambient temperature, relative humidity, soil temperature, and rainfall—via deployed sensor nodes managed by an ESP32 microcontroller pipeline. The telemetry stream is transmitted through low-power communication channels to a centralized processing engine, where a Random Forest classification model evaluates multidimensional soil-environmental interactions to predict immediate irrigation requirements. The predicted states feed into an automated actuation layer that directly modulates a solenoid-valve and water-pump relay, forming a closed-loop feedback pipeline. Evaluated against traditional threshold-based and schedule-driven approaches, the proposed IoT-ML framework demonstrates superior operational responsiveness, minimizes unnecessary water application, and offers a robust, scalable architectural template for modern precision agriculture.

Authors: Dr. M. Rathamani 

Keywords: Internet of Things (IoT), Smart Irrigation, Soil Moisture Sensors, Machine Learning, Random Forest, Precision Agriculture, Automated Control, Sustainable Water Management.