YIELD PREDICTION AND ANALYSIS USING IOT DATA

Abstract

The accurate prediction and analysis of crop yield are essential for optimizing agricultural practices, ensuring food security, and minimizing resource wastage. In this study, we present an IoT-based framework for yield prediction, leveraging data from interconnected sensors deployed across crop fields. These IoT devices collect real-time data on soil conditions (e.g., moisture, nutrient content), environmental factors (e.g., temperature, humidity, rainfall), and plant health indicators. By integrating this sensor data with historical yield information and leveraging machine learning algorithms, our approach offers predictive insights into potential crop yields. This IoT-driven system enables farmers to monitor crop growth stages, anticipate harvest outcomes, and make informed decisions regarding irrigation, fertilization, and pest control. Furthermore, the framework’s predictive accuracy empowers farmers to enhance resource management and reduce operational costs while contributing to sustainable agricultural practices. Initial results demonstrate that this IoT-based yield prediction system provides robust, data-driven support for precision agriculture, enabling more reliable yield forecasts and improving overall farm productivity.