基于深度迁移学习的小样本茶叶嫩芽识别

朱绍辉; 赵文举; 马博慧; 杨化林; 邓芳

doi:10.13305/j.cnki.jts.2025.03.001

茶叶科学 >

2025 , Vol. 45 >Issue 3: 522 - 534

DOI: https://doi.org/10.13305/j.cnki.jts.2025.03.001

研究报告

基于深度迁移学习的小样本茶叶嫩芽识别

朱绍辉 ,
赵文举 ,
马博慧 ,
杨化林 ,
邓芳

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青岛科技大学机电工程学院,山东青岛 266061

朱绍辉,男,硕士研究生,主要从事图像处理和目标检测方面的研究。

收稿日期: 2024-11-19

修回日期: 2024-12-25

网络出版日期: 2025-06-18

基金资助

国家自然科学基金（52101401）、山东省重点研发计划（2024CXPT033）、湖北省重点实验室开放基金（KFJJ-2022012）

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Research on Tea Bud Recognition Technology Based on Deep Transfer Learning

ZHU Shaohui ,
ZHAO Wenju ,
MA Bohui ,
YANG Hualin ,
DENG Fang

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College of Mechanical and Electrical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China

Received date: 2024-11-19

Revised date: 2024-12-25

Online published: 2025-06-18

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摘要

为减少重复构建不同季节、不同地区茶叶嫩芽数据样本的时间成本,提高利用深度学习模型识别不同茶叶嫩芽的精度和泛化性,提出了一种基于深度迁移学习的小样本茶叶嫩芽识别模型QY-Yolov7-Tea。通过将迁移学习技术引入深度学习,构建源域嫩芽Yolov7检测模型获取预训练权重,针对Yolov7模型的骨架、颈部、检测头部分进行微调、冻结,并依据预训练权重针对不同目标样本进行试验验证,最终形成嫩芽迁移模型。结果表明,与传统Yolov7嫩芽检测模型相比,深度迁移学习模型在对不同季节和不同地区茶叶嫩芽识别任务中平均精度均值分别提升了8.8个百分点和15.4个百分点,显著改善了模型的鲁棒性和识别能力,有效应对了训练样本不足的问题。

关键词： 茶叶嫩芽; 深度学习; 迁移学习; 模型微调

本文引用格式

朱绍辉 , 赵文举 , 马博慧 , 杨化林 , 邓芳 . 基于深度迁移学习的小样本茶叶嫩芽识别[J]. 茶叶科学, 2025 , 45(3) : 522 -534 . DOI: 10.13305/j.cnki.jts.2025.03.001

Abstract

To reduce the time cost of repeatedly constructing data samples of tea buds from different seasons and regions, and to improve the accuracy and generalization ability of deep learning models in identifying different tea buds, this paper proposed a small-sample tea bud recognition model based on deep transfer learning, named QY-Yolov7-Tea. By incorporating transfer learning techniques into deep learning, a source domain bud detection model based on Yolov7 was used to obtain pre-trained weights. The model's backbone, neck and detection head are then fine-tuned and frozen, with experiments conducted on different target samples based on the pre-trained weights. The final result is a transfer learning model for tea buds. Experimental results show that, compared with the traditional Yolov7 bud detection model, the deep transfer learning model improves the mean average precision for recognizing tea buds from different seasons and regions by 8.8 percentage points and 15.4 percentage points, respectively, significantly enhancing the model's robustness and recognition capability, and effectively addressing the issue of insufficient training samples.

Key words： tea bud; deep learning; transfer learning; model fine-tuning

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