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Study of Machine Learning Based Rice Breeding Decision Support Methods and Technologies

  • Yun-peng CuiEmail author
  • Jian Wang
  • Shi-hong Liu
  • En-ping Liu
  • Hai-qing Liu
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 545)

Abstract

The Objective of the study is to Analyze and mining rice breeding data with data explore and machine learning algorithms to discover how rice biological characters influence the economic characters, explore effective methods and technologies for breeders and help them find appropriate breeding parents, and provide tools for parental selection in rice breeding. The author developed a B/S application with Python and Django, which implement real-time data mining of rice breeding data. Data analysis and processing result generated from decision tree algorithm can find effective breeding knowledge and patterns, and spectral biclustering algorithm can find required varieties with their local features follow certain patterns. The system can help breeders find useful knowledge and patterns more quickly, and improves the accuracy and efficiency of crop breeding.

Keywords

Machine learning Rice Breeding Decision support 

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Yun-peng Cui
    • 1
    Email author
  • Jian Wang
    • 1
  • Shi-hong Liu
    • 1
  • En-ping Liu
    • 2
  • Hai-qing Liu
    • 2
  1. 1.Agricultural Information Institute, Chinese Academy of Agricultural Sciences/Key Laboratory of Agri-Information Service TechnologyMinistry of AgricultureBeijingPeople’s Republic of China
  2. 2.Institute of Scientific and Technical InformationCATS/Key lab of Tropical Crops Information Technology Application Research of Hainan ProvinceDanzhouPeople’s Republic of China

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