基于YOLOv5s的草莓病害识别系统设计

Design of Strawberry Disease Identification System Based on YOLOv5s

摘要: 草莓种植易受到20多种病害的影响，目前草莓病害识别主要以人工为主，耗时费力，效率较低。因此，文章基于YOLOv5s目标检测算法（YOLO You Only Look Once:Unified, Real-Time Object Detection），对采集到的696张草莓病害图片进行模型训练，设计草莓病害识别系统，实验结果显示系统识别精度接近80%，相较于传统草莓病害识别技术更便捷且识别效率更高。

Abstract: Strawberry is easily affected by more than 20 diseases during planting. At present, strawberry disease identification is mainly manual, time-consuming and laborious, with low efficiency. Therefore, based on the yolov5s image recognition algorithm,this paper conducts model training on 696 collected strawberry disease images, and designs a strawberry disease recognition system.The experimental results showed that the recognition effect of the system is good.