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精准农业的YOLOv11n:轻量级和高效的检测瓜子缺陷在各种条件下

Madasamy Raja Ganapathy¹, Selvaraju Periasamy², Pathmanaban Pugazhendi³

¹Department of Information Technology, Paavai Engineering College, Namakkal, India.

Journal of the science of food and agriculture

|May 5, 2025

概括

YOLOv11n模型在自动化瓜缺陷检测方面表现出卓越的性能,提供高精度和实时处理,以改善农业质量评估,即使在资源有限的环境中.

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:

自动水果缺陷检测对于农业供应链的收获后质量评估至关重要.
果缺陷检测是具有挑战性的,因为各种疾病,成熟度水平和环境条件.
现有的只看一次 (YOLO) 模型在资源受限的设置中,难以平衡准确性,速度和效率.

研究的目的:

评估四个YOLO模型 (YOLOv8s,YOLOv5s,YOLOv9s,YOLOv11n) 的性能,以检测有缺陷的瓜.
确定最适合于实时,在各种农业条件下准确检测缺陷的YOLO模型.
在资源有限的环境中解决当前模型的局限性.

主要方法:

培训和评估了YOLOv8,YOLOv5,YOLOv9和YOLOv11n模型,用于瓜缺陷检测.
测试了五种疾病,三种成熟度和健康水果的模型.
基于mAP50-95的评估模型,精度,回忆,推断时间,检测速度,参数和计算成本.

主要成果:

YOLOv11n实现了最高的mAP50-95 (98.0%),具有最佳的损失指标和快速推断 (3.9 ms,255 FPS).
YOLOv5s显示了最高的精度 (94.9%),而YOLOv9s在召回方面领先 (96.2%).
YOLOv11n提供轻量级架构 (2.6M参数) 和低计算成本 (6.3 GFLOPS),非常适合资源有限的应用程序.

结论:

YOLOv11n显示了自动化农业缺陷检测和质量控制的巨大潜力,满足了对高精度和实时性能的要求.
这些发现为在农业质量评估中部署YOLO模型提供了宝贵的见解,提高了收获后管理效率和可靠性.

关键词:

计算效率的计算效率深度学习是一种深度学习.图像的分类图像的分类.神经网络的神经网络的神经网络对象检测检测对象检测对象检测精准农业精准农业精准农业

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