Optimizing Crop Selection with Bigdata and IoT Sensor Technology in Modern Agriculture by Univariate Feature Selection

In order to enhance agricultural practices, the integration of cutting-edge technologies is examined in the research “Optimizing Crop Selection with Big Data and IoT Sensor Technology in Modern Agriculture by Univariate Feature Selection”. It focuses on optimizing crop selection through the use of Internet of Things (IoT) sensor technology and big data analytics. A statistical method called univariate feature selection aids in determining the most important variables influencing crop development and yield. Real-time data on crop conditions, weather, and soil conditions are gathered with IoT devices and evaluated with big data techniques. Through data filtering, univariate feature selection identifies critical characteristics that affect crop success. With this method, farmers can choose crops depending on market demand and environmental factors by using data to inform their selections. Big data and Internet of Things (IoT) technology work together to give farmers meaningful insights that improve productivity, profitability, and sustainability. Accurate crop selection highlights the revolutionary potential of these cutting-edge techniques in contemporary agriculture by maximizing resource utilization, cutting waste, and improving yields.