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Improved GA-SVM Algorithm and Its Application of NIR Spectroscopy in Orange Growing Location Identification

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Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1088))

Abstract

In order to establish a more accurate and efficient orange growing location identification model, we proposed Genetic Algorithm Support Vector Machine (GA-SVM) that based on Support Vector Machine (SVM). This algorithm combines genetic optimization options to improve the SVM algorithm, and the experimental results indicate that GA-SVM can significantly improve the prediction rate of SVM.

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Acknowledgements

This work was supported by China Scholarship Council (CSC), Advanced Robotics and Intelligent Systems (ARIS) Laboratory in the School of Engineering at the University of Guelph. The authors thank Qiuhong Liao for providing the data used in this research work.

Project: The project of Chongqing University of Education “study on orange growing location identification technology based on near infrared spectroscopy” (Project Number: KY201711B).

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Authors and Affiliations

  1. College of Continuing Education, Chongqing University of Education, Chongqing, China

    Songjian Dan

  2. School of Engineering, University of Guelph, Guelph, ON, Canada

    Simon X. Yang

Authors
  1. Songjian Dan
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  2. Simon X. Yang
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Corresponding author

Correspondence to Songjian Dan .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Chuanchao Huang

  2. Computer Science and Information Management, Providence University, Taichung, Taiwan

    Yu-Wei Chan

  3. University of Aizu, Fukushima, Japan

    Neil Yen

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Dan, S., Yang, S.X. (2020). Improved GA-SVM Algorithm and Its Application of NIR Spectroscopy in Orange Growing Location Identification. In: Huang, C., Chan, YW., Yen, N. (eds) Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Advances in Intelligent Systems and Computing, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-15-1468-5_70

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