基于卷积神经网络模型的鲜茶叶分选技术研究

Zhu Yanyan, Fu Maosheng, Shi Yun
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引用次数: 1

摘要

针对传统茶叶分类方法难以准确细分茶叶的问题,设计了基于计算机视觉技术的卷积神经网络(CNN)模型。该模型有三个卷积层,两个池化层和一个全连接层。通过对真实拍摄的火山黄崖茶图像的训练和测试,该模型识别火山黄崖茶的正确率达到95.3%。实验结果表明,基于卷积神经网络模型的鲜茶分选技术解决了依赖人工分选混合不同类型鲜茶的问题,提高了霍山黄崖茶的品质和价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on sorting technology of fresh tea leaves based on convolutional neural network model
A convolutional neural network (CNN) model was designed based on computer vision technology, given the difficulty in accurately subdividing tea leaves traditional sorting methods. The model has three convolutional layers, two pooling layers, and one fully connected layer. Through the training and testing of the real shot Huoshanhuangya teas image, the correct rate of the model to identify Huoshanhuangya teas reached 95.3%. The experimental results show that the fresh tea leaves sorting technology based on the convolutional neural network model solves the problem of relying on manual sorting and mixing of different types of fresh tea, and improves the quality and value of Huoshanhuangya teas.
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