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使用YOLOv8 tinier和RepNCSP ELAN实时检测甘茎的健康状况

Pushpendra Kumar^1,2, Shraddha Arora³, Shaveta Arora³

¹Department of Computer Science and Engineering, The Northcap University, Gurugram, India. pushpendra22csd010@ncuindia.edu.

Scientific reports

|December 12, 2025

概括

一个新的轻量级物体检测模型RepNCSP-ELAN-YOLOv8n,可以实时分类甘干的健康状况. 这一进步对于自动芽提取至关重要,改善了种子质量和农业作物产量.

科学领域:

农业技术农业技术
计算机视觉计算机视觉计算机视觉
机器学习是机器学习.

背景情况:

甘是全球主要的作物,对于糖和生物燃料生产至关重要.
自动提取芽进行繁殖需要精确的甘茎健康检测.
现有的模型对于农业中资源有限的边缘设备缺乏效率.

研究的目的:

开发一种轻量级的物体检测模型,用于实时分类甘干的健康状况.
解决用于农业自动化的自动化甘茎健康检测缺口.
为了在边缘设备上实现高效部署,以提高作物产量.

主要方法:

介绍了RepNCSP-ELAN-YOLOv8n,这是一个新的轻量级物体检测模型.
在一个自定义的数据集上训练模型,其中包括来自印度不同农场条件的3839张图像.
与已建立的YOLO版本进行基准测试,并在边缘AI平台上进行验证.

主要成果:

该RepNCSP-ELAN-YOLOv8n模型实现了7.2GFLOPs,2.2M参数和4.6MB的大小.
实现了最先进的性能,90.1%的mAP@50和14.33ms的推理时间.
与其他纳米级YOLO模型相比,证明了更高的效率和更高的准确性.

结论:

拟议的模型提供了一个高效和准确的解决方案,用于实时检测甘茎健康状况.
允许在边缘设备上进行实际部署,推进智能农业自动化.
有助于提高种子质量和提高甘农业作物生产率.

关键词:

杰伦·格兰 (Gelan Gelan) 是一个著名的演员.实时检测甘干的健康状况 RepNCSP-ELANAN 的时间表.这是一个YOLO YOLO.

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