Machine Learning Algorithms For Predicting Crop Health Using IoT-Generated Agriculture Data

Abstract

This paper explores the integration of Internet of Things (IoT) technology and machine learning algorithms for predicting crop health in agriculture. The study investigates the use of various sensors and IoT devices to collect real-time data on environmental factors and crop conditions. Different machine learning algorithms, including supervised and regression techniques, are employed to analyze the collected data and make predictions regarding crop health and potential disease outbreaks. The research aims to enhance agricultural productivity and sustainability by providing farmers with timely insights for proactive decision-making. Experimental results demonstrate the effectiveness of the proposed approach in accurately predicting crop health and mitigating risks associated with crop diseases.