EARLY PEST DETECTION

Abstract

 Early pest detection is a critical component of
modern agricultural practices, aiming to minimize the
damage caused by pests and improve crop yield. With
the increasing need for sustainable farming techniques,
traditional pest control methods are being replaced by
advanced technologies that offer more precise and
efficient solutions. This study explores the use of
innovative techniques such as machine learning, image
processing, and sensor technologies to detect pests at
early stages in agricultural fields. By using highresolution
imagery, sensors, and automated systems,
early signs of pest infestations can be identified,
allowing for targeted and timely interventions. The
integration of artificial intelligence (AI) models, such
as convolutional neural networks (CNNs), enables the
system to recognize patterns indicative of pest presence,
reducing the reliance on chemical pesticides and
minimizing environmental impact. The goal of this
research is to enhance pest management strategies
through early detection, improving crop protection,
reducing costs, and promoting sustainable agricultural
practices. The findings aim to contribute to the
development of an automated, efficient, and ecofriendly
pest management system for the agriculture
industry.