Revolutionizing Plant Disease Detection in Agriculture: a Comparative Study of Yolov5 and Yolov8 Deep Learning Models
- Posted
- Server
- Research Square
- DOI
- 10.21203/rs.3.rs-3936780/v1
Object detection stands as a pivotal task within computer vision, finding extensive use across various domains. Recent years have witnessed a transformative shift in object detection thanks to deep learning methodologies, with You Only Look Once(YOLO) emerging as a prominent algorithm in this field. In this research paper, our focus lies in conducting an in-depth comparative analysis between two advanced deep learning models, You Only Look Once Version 5(YOLOv5) and You Only Look Once Version 8 (YOLOv8), to assess their applicability in the context of plant leaf disease detection within the agricultural sector. Our results unequivocally establish YOLOv8 as the superior performer, exhibiting exceptional precision, recall, and class differentiation, and notably, outperforming YOLOv5 by approximately 3% in mean average precision (mAP). This study demonstrates the prowess of YOLOv8 as a state-of-the-art object detection algorithm, offering implications for diverse applications beyond agriculture.