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Automatic Recognition of Tea Bud Image Based on Support Vector Machine

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Book cover Advanced Hybrid Information Processing (ADHIP 2020)

Abstract

The existing recognition method of tea shoots is only to judge the single color or shape features, resulting in low recognition accuracy. Therefore, an automatic recognition method of tea shoots image based on support vector machine is designed. In this method, two kinds of image features, color and shape texture, are extracted from the tea bud image for discrimination. The RGB model is used to extract color features, and LBP/C operator is used to extract the shape and texture features of the bud. The extracted features are used as the feature vectors of the training samples, and support vector machine model training is carried out to obtain the support vector machine classifier, and the tea bud image is recognized. The experimental results show that the recognition rate, recall rate and comprehensive evaluation index of the method are higher than those of the traditional method, which proves that the method has high recognition accuracy and improves the recognition efficiency.

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Funding

Youth science and technology talent growth project of Guizhou Provincial Education Department (Qian Education KY [2019]181, Qian Education KY [2016]298), Science and technology cooperation project of Guizhou Provincial Science and Technology Department (Qian Science LH [2016] 7288).

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Correspondence to Yuan-yuan Gao .

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Li, W., Chen, R., Gao, Yy. (2021). Automatic Recognition of Tea Bud Image Based on Support Vector Machine. In: Liu, S., Xia, L. (eds) Advanced Hybrid Information Processing. ADHIP 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-030-67874-6_26

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  • DOI: https://doi.org/10.1007/978-3-030-67874-6_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67873-9

  • Online ISBN: 978-3-030-67874-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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