Enhancing Crop Water Management: A Logistic Regression Approach Integrated with IoT for Smart Irrigation

Abstract

This study explores the integration of logistic regression with Internet of Things (IoT) technology to optimize water management in agriculture. Efficient irrigation systems are vital for boosting crop yields while preserving water resources, which is becoming more important due to the growing demand for food and the challenges caused by climate change. The suggested method makes use of Internet of Things (IoT) sensors to gather weather predictions, soil moisture levels, humidity, and temperature readings in real time. In order to determine how much water crops will use, this data is utilized to train a logistic regression model. Supervised learning is made possible by generating labelled data through the analysis of expert knowledge and past irrigation practices. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. Once validated, the logistic regression model is deployed within the IoT system to provide real-time predictions of crop water requirements. Through automation and data-driven decision-making, farmers can optimize irrigation schedules, minimize water wastage, and enhance crop productivity. This integrated approach represents a significant step towards sustainable agriculture and resource-efficient farming practices.