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基于ResNet卷积神经网络的绿茶种类识别模型构建

Construction of Green Tea Recognition Model Based on ResNet Convolutional Neural Network

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【作者】 张怡赵珠蒙王校常冯海强林杰

【Author】 ZHANG Yi;ZHAO Zhumeng;WANG Xiaochang;FENG Haiqiang;LIN Jie;The Key Laboratory for Quality Improvement of Agricultural Product of Zhejiang Province, Zhejiang A&F University;Institute of Tea Science, Zhejiang University;Planting Administration Bureau of Zhejiang Province;

【通讯作者】 林杰;

【机构】 浙江农林大学农业与食品科学学院浙江省农产品品质改良技术研究重点实验室浙江大学茶叶研究所浙江省种植业管理局

【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。

【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.

【基金】 国家自然科学基金(31800582);浙江省农业重大技术协同推广计划(2020XTTGCY03)
  • 【文献出处】 茶叶科学 ,Journal of Tea Science , 编辑部邮箱 ,2021年02期
  • 【分类号】TP183;TP391.41;TS272.51
  • 【被引频次】10
  • 【下载频次】877
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