Pest and Disease Detection and Classification Using YOLOv8

To achieve real time detection and classification of pests to increase agricultural productivity the system is proposed to be a YOLOv8 based pest detection system. Images of agricultural fields and storage are processed by the system based on these captured by drones and stationary cameras and a pest is detected with high speed and accuracy. The system combines a large computational power and high speed performance of the YOLOv8 algorithm to precisely locate the pest in time to provide timely intervention and targeted pest control. In addition, it not only saves damage to crops but also it improves pest management and thus makes the farm perform well using a sustainable farming approach. Most suitable for different agricultural situations with less application and support for more sustainable agricultural practices because of the system’s capacity to adapt to changing environmental conditions and pest population dynamics.