The Outcomes of Smart Irrigation System using Machine Learning to minimize water usage within the Agriculture Sector

The water scarcity challenge has become a global threat to food security and the entire agricultural value chain at large. Saving water is the most strenuous challenge for farmers prompting the need for smart irrigation infrastructure upgrades. To predict the best time to irrigate or not, farmers utilize information such as soil type, available water resources, soil moisture, and climate conditions to make decisions that solve agricultural complexity. Deploying field sensors using IoT, a data-driven technology, requires the combination of emerging technologies and modern methodologies to provide solutions to the complex problems faced by agriculture. IoT has facilitated the gathering of information over a long period, and since information is accessible, the implementation of 4IR in the sector of agriculture to machine learning and Logistic Regression identifies several perceptions that lead to the solution for a complex problem. This article proposes a 4IR-enabled Smart Irrigation System that minimizes water usage within the agriculture sector to assist farmers in getting live data of soil moisture, weather API, the water capacity of different crops, and plugging sensors to predict when farmers can irrigate or not.