CNN-Driven Pest Recognition System for Precision Crop Protection

Author : IKKURTHI SRINIVASA RAO

Abstract : Pest infestation is a major factor affecting crop productivity and quality in modern agriculture. Traditional pest detection methods are manual, time-consuming, and prone to human error, leading to delayed interventions. This paper presents an AI-powered pest detection system that uses deep learning and computer vision techniques to automatically identify pests in crop images. A Convolutional Neural Network (CNN) model is trained on a large dataset of pest images to recognize multiple pest classes with high accuracy. The system supports real-time detection and can be integrated with mobile devices, drones, or IoT-based field monitoring systems for continuous surveillance. Experimental results demonstrate that the proposed approach significantly improves early pest identification, reduces crop losses, and enhances decision-making in precision agriculture. This work highlights the potential of AI driven solutions for sustainable crop protection and smart farming applications.

Keywords : Artificial Intelligence, Deep Learning, Pest Detection, Convolutional Neural Networks (CNN), Computer Vision, Precision Agriculture, Crop Protection, Image Classification, Smart Farming, Automated Monitoring.

Conference Name : International Conference on Intelligence and Safety for Robotics (ICISR - 25)

Conference Place : Faridabad, India

Conference Date : 27th Dec 2025