IoT for Real-Time Pest Detection in Agriculture

Abstract

The growing infestation of pests endangers agricultural productivity, resulting in lower yields and financial losses. Conventional pest detection techniques are usually inefficient and reactive. This study investigates the application of IoT-based sensors and data analytics for real-time pest monitoring, increasing accuracy and allowing predictive modelling. Through the combination of machine learning, wireless sensor networks, and cloud computing, the proposed system equips farmers with timely, targeted pest control actions. This method encourages sustainable agriculture by reducing the use of pesticides and crop damage, providing a scalable solution for precision agriculture.