一个改进的YOLOv8n模型用于在pakchoi中现场检测害虫和疾病
Yi Zhu1,2, Yanlu Han1,2, Yilu Yin3
1College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, China.
Frontiers in plant science
|February 9, 2026
概括
一个新的轻量级物体检测模型,YOLOv8n-DBW,准确地识别了pakchoi害虫和疾病. 这种改进的模型提高了实时农业应用的检测精度和效率.
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:
- 帕克乔伊 (Brassica chinensis L.) 易受害虫和疾病的影响,导致大量的产量和质量损失.
- 准确有效地检测这些问题对于有效的作物管理至关重要.
研究的目的:
- 开发一个改进的轻量级物体检测模型,用于检测pakchoi害虫和疾病.
- 提高现场环境中的害虫和疾病识别的效率和准确性.
主要方法:
- 提出了一个改进的基于YOLOv8n的模型,YOLOv8n-DBW,结合了具有部分卷积 (PConv) 和高效多尺度注意力 (EMA) 的新型C2f-PE模块.
- 集成了一个加权双向特征金字塔网络 (BiFPN) 进行增强的多尺度特征融合.
- 实施了Wise-IoU限制框损失,以提高回归精度,特别是对于小目标.
主要成果:
- 与基线YOLOv8n.DBW相比,YOLOv8n-DBW实现了参数减少33.3%,模型大小减少31.8%.
- 在精度 (+5.0%) 和平均平均精度 (mAP) (+7.5%) 中得到了明显的改善.
- 在检测pakchoi害虫和疾病方面表现优于几种主流对象检测算法.
结论:
- YOLOv8n-DBW模型提供了一种高效准确的解决方案,用于实时检测pakchoi害虫和疾病.
- 该模型显示了在农业中部署嵌入式系统和移动设备的巨大潜力.
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