基于ResNet卷积神经网络的绿茶种类识别模型构建

doi:10.13305/j.cnki.jts.2021.02.008

摘要/Abstract

摘要： 绿茶是我国种类最多、产量最大的茶类,外形是其分类的重要依据。图像分类是计算机视觉的核心技术之一,但其在茶叶领域的应用较少,茶类识别仍依赖感官审评方法。采集8种常见绿茶（丽水香茶、信阳毛尖、六安瓜片、太平猴魁、安吉白茶、碧螺春、竹叶青和龙井）共1 713张图片,基于ResNet卷积神经网络,从识别模型的预测能力、收敛速度、模型大小和识别均衡性等角度探索了不同网络深度和不同优化算法的建模效果,最终选择ResNet-18结构、SGD优化算法,建立了区分8种绿茶的深度学习模型,其对复杂背景茶叶图片的识别准确率达到了90.99%,单张图片识别时间仅为0.098 s,模型大小为43.7 MB。本研究为构建茶叶视觉识别模型并应用于移动端提供了基础,为茶叶种类识别提供了一种快捷而高效的新方法。

关键词: 卷积神经网络, 深度学习, 绿茶分类

Abstract: Green tea is the tea with the largest variety and output in China. Its appearance is an important basis for its classification. Image classification is one of the core technologies of computer vision, but its application in tea field is almost blank. Tea recognition still relies on the sensory evaluation methods by experts. This study collected 1713 pictures of 8 kinds of green tea (Lishui Xiangcha, Xinyang Maojian, Lu'an Guapian, Taiping Houkui, Anji Baicha, Biluochun, Zhuyeqing and Longjing). Based on the convolutional neural network, we explored the effects of ResNet model depth from the perspectives of model convergence speed, size, efficiency and identification balance. Finally, the ResNet-18 and SGD optimization algorithms were selected and a deep learning model was established to distinguish 8 kinds of green tea. The accuracy reached 90.99%, the recognition time of the single picture was only 0.098 s, and the model size was 43.7 MB. This paper provided the foundation for constructing a tea visual recognition model and applying it to the mobile terminals and provided a new accurate and efficient method for tea recognition.

Key words: convolutional neural network, deep learning, classification of green tea

中图分类号:

S571.1
TP183

张怡, 赵珠蒙, 王校常, 冯海强, 林杰. 基于ResNet卷积神经网络的绿茶种类识别模型构建[J]. 茶叶科学, 2021, 41(2): 261-271. doi: 10.13305/j.cnki.jts.2021.02.008.

ZHANG Yi, ZHAO Zhumeng, WANG Xiaochang, FENG Haiqiang, LIN Jie. Construction of Green Tea Recognition Model Based on ResNet Convolutional Neural Network[J]. Journal of Tea Science, 2021, 41(2): 261-271. doi: 10.13305/j.cnki.jts.2021.02.008.

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