Integrating Iot And Machine Learning For Sustainable Water Management In Agriculture

Abstract

The imperative for sustainable water management in agriculture has never been more acute, given the escalating challenges of water scarcity, climate change, and the growing global demand for food production. This paper explores the integration of the Internet of Things (IoT) and Machine Learning (ML) as a revolutionary approach to optimizing water usage in agriculture. By harnessing the power of real-time data collection through IoT sensors and applying ML algorithms to predict water needs accurately, this research aims to provide a sustainable, efficient solution for water management. We present a comprehensive system design that incorporates IoT devices for continuous monitoring of soil moisture, weather conditions, and crop water usage, alongside ML models that process this data to make predictive analyses for irrigation. The effectiveness of this integration is evaluated through a series of tests, comparing traditional water management practices against our IoT and ML-based approach. Results indicate a significant improvement in water use efficiency, demonstrating the potential of such technologies to transform agricultural practices. This study not only contributes to the academic discourse on smart agriculture but also offers practical insights for farmers and policymakers seeking to adopt more sustainable water management practices.