Design and experiment of an intelligent fresh tea leaves grading device

doi:10.13733/j.jcam.issn.2095-5553.2025.03.021

Abstract

Abstract:

To realize the online grading of Longjing tea fresh leaves of different quality levels, this study proposes a machine vision-based method for online detection and grading of fresh tea leaves, and designs and manufactures an intelligent grading device. By adding a coordinate attention mechanism, incorporating dilated spatial convolution pooling pyramid, and improving the feature fusion network, YOLOv5s is optimized into YOLOv5s—CAB. The average precision for recognition is 90.4%, and the recall rate is 87.8%. Experiments conducted on the fresh tea leaves grading device show that when the optimal parameters for the falling speed of the leaves and the conveyor belt speed are set at 2.08 g/s and 150.00 mm/s, respectively, the recognition precision reaches 95.58%, which verifies the feasibility and reliability of the device and provides technical support for the intelligent grading of fresh tea leaves.

Key words: fresh tea leaves, deep learning, real-time detection, intelligent grading

摘要：

为实现不同等级的龙井茶鲜叶在线分级，提出一种基于机器视觉的茶鲜叶在线检测与分级方法，设计并制造一套智能分级装置。通过添加坐标注意力机制、引入空洞空间卷积池化金字塔和改进特征融合网络对YOLOv5s进行优化得到YOLOv5s—CAB，识别的平均精度均值为90.4%，召回率为87.8%。在茶鲜叶分级装置上进行试验，结果表明：确定最佳参数茶鲜叶下落速度和传送带速度分别为2.08 g/s、150.00 mm/s时，识别的准确率达95.58%，验证装置的可行性与可靠性，为茶鲜叶的智能化分级提供技术支撑。

关键词: 茶鲜叶, 深度学习, 实时检测, 智能分级

CLC Number:

Wu Jian, , Ye Mengyan, Zhang Tongfeng. Design and experiment of an intelligent fresh tea leaves grading device [J]. Journal of Chinese Agricultural Mechanization, 2025, 46(3): 139-145.

吴坚, , 叶梦焱, 张同锋. 茶鲜叶智能分级装置设计与试验[J]. 中国农机化学报, 2025, 46(3): 139-145.

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