使用增强的YOLOv12框架检测和分类新鲜茶叶的细粒度检测
Shuang Zhao1, Chun Ye1, Chentao Lian2
1Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China.
Foods (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了一种增强的YOLOv12深度学习模型,用于智能分类新鲜茶叶. 改进的框架准确地识别了微妙的茶叶芽,提高了茶叶加工效率的分类准确性.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 茶的质量与新鲜的叶子质量直接相关,需要先进的分类方法.
- 由于复杂的形态和小,密集的目标,传统的分类与机器挑选的茶叶作斗争.
- 现有的智能分级系统在准确的识别和一致性方面面临挑战.
研究的目的:
- 开发一个增强的深度学习框架,以准确和稳健地检测优质茶叶芽.
- 提高新鲜茶叶的分类准确度和一致性,特别是机器采集样本.
- 为了应对细粒度差异,小物体大小和茶叶芽图像中复杂的背景等挑战.
主要方法:
- 提出了一个增强的YOLOv12检测框架,集成C3k2_EMA,A2C2f_DYT和RFAConv模块.
- 专注于加强模型捕捉微妙茶叶芽特征的能力.
- 利用机器视觉和深度学习进行新鲜茶叶的自动分类和分类.
主要成果:
- 在优质茶的识别中获得了81.2%的精度,90.6%的回忆和92.7%的mAP@0.5.
- 在检测精度和稳定性方面显著改进,用于细粒度茶叶芽识别.
- 有效地支持智能和高效的茶叶收获和分拣操作.
结论:
- 增强的YOLOv12模型为智能新鲜茶叶分类提供了技术上可行的解决方案.
- 提出的方法有效地解决了当前自动茶叶分拣系统的局限性.
- 这一进步支持茶叶行业的精确质量控制和生产监测.
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