Enhancing Precision Agriculture: YOLOv8 for Accurate Corn Disease and Pest Detection

Corn is a crucial staple crop globally, with its production facing challenges due to the prevalence of pests and diseases. This paper presents the use of YOLOv8 (You Only Look Once version 8) algorithm, an advanced object detection technology, to identify and classify corn diseases such as gray leaf spot and southern rust. With deep convolutional networks, batch normalization layers, and attention mechanisms, YOLOv8 offers high accuracy and real-time processing even in complex environments. Experimental results show an 80.44% accuracy in disease detection, reducing false negatives and positives, thereby enhancing agricultural control efficiency and crop yield. Moreover, YOLOv8 facilitates precision agriculture, leading to reduced pesticide use, lower production costs, and less environmental impact. The paper also explores potential applications of YOLOv8 in pest and disease detection across various crops and proposes future research directions for model adaptive learning and integration with smart agricultural systems. Through technical descriptions and real-world application analyses, this study offers insights for smart agricultural technologies and future research in this field.