An Intelligent Detection and Identification System for Crop Pests and Diseases Based on YOLOv8

Addressing the limitations of traditional manual detection for crop pests and diseases, such as its subjectivity and low efficiency, this study designs and implements an intelligent detection and identification system based on the YOLOv8 algorithm to enhance the automation and accuracy of field diagnosis. The system adopts a modular architecture, encompassing input output, data preprocessing, model training and optimization, pest disease identification and localization, and a user interface module. The data preprocessing module generates high quality training sets through image annotation and data augmentation, laying the foundation for model performance. The model training module incorporates various optimization strategies to enhance detection capabilities. The identification module can output the category, confidence, and location information of pests and diseases in real time. Experimental results demonstrate that the system performs excellently in multi class pest and disease identification tasks, exhibiting high reliability and strong practical utility. It provides technical support for agricultural pest and disease control and holds broad prospects for widespread adoption. Chen Yuxuan | Liu Fangbin | Gao Jian "An Intelligent Detection and Identification System for Crop Pests and Diseases Based on YOLOv8" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-6 , December 2025, URL: https://www.ijtsrd.com/papers/ijtsrd98709.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/98709/an-intelligent-detection-and-identification-system-for-crop-pests-and-diseases-based-on-yolov8/chen-yuxuan