基于改进的YOLOv8n模型的葡萄叶疾病识别方法的研究
Huiping Guo1, Jiarui Cao1, Yi Wang1
1College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China.
Frontiers in plant science
|March 2, 2026
概括
这项研究引入了一种改进的YOLOv8n模型,用于葡萄叶病的识别,以更小的尺寸和更快的处理速度实现高精度. 改进后的模型有助于精准农业,通过快速准确地识别葡萄园中的疾病.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 葡萄叶病的识别模型经常受到大尺寸和有限的分类能力的影响.
- 现有的模型在现实世界葡萄园条件下难以提高效率和准确性.
研究的目的:
- 开发一种增强的葡萄叶疾病识别模型,其准确度提高,尺寸缩小,推断速度更快.
- 解决现有模型在精密农业中的实际应用的局限性.
主要方法:
- 通过整合G-bneck和simSPPF模块来改进YOLOv8n架构,以提高功能提取和速度.
- 引入了C2F-UIB模块,以减少模型参数和计算负载.
- 集成的LInner-CIoU损失和部分卷积,用于加速推断和强大的图像处理.
主要成果:
- 增强的YOLOv8n模型实现了97.3%的准确性,3.53MB的模型大小和228.55 FPS.
- 在平均精度方面表现优于YOLOv3-tiny,YOLOv5n和YOLOv6n.
- 在喷雾装置上演示了89.3%的准确性和5.18秒的处理时间.
结论:
- 拟议的模型有效地克服了葡萄叶疾病识别的规模和速度的挑战.
- 允许在自然环境中准确和快速识别葡萄叶病.
- 在精准农业中开发有效的葡萄疾病管理技术具有很大的潜力.
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