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)


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.


Machine learning Rice Breeding Decision support 


  1. 1.
    Zhu, R., Deng, J., Li, Y.: The response of rice yield and nitrogen fertilizer utilization to different formula fertilizer. Mod. Agric. (10), 17–21 (2008). (in Chinese)Google Scholar
  2. 2.
    Zhu, R.S., Deng, J.S., Li, Y.: Response of rice yield and nitrogen fertilizer utilization rate to different recipes fertilizer. Mod. Agric. (10), 17–21 (2008). (in Chinese)Google Scholar
  3. 3.
    Xia, R.B.: A study on the breeding science and technology of rice in contemporary China. Nanjing Agricultural University (2009). (in Chinese)Google Scholar
  4. 4.
    Che, S.F., Dai, K.K., Cao, F.L.: The hybrid training algorithm for feedforward neural networks and its application. J. China Univ. Metrol. (4), 424–431 (2014). (in Chinese)Google Scholar
  5. 5.
    Yan, D.C., Zhu, Y., Cao, W.X.: A knowledge model for selection of suitable variety in rice production. J. Nanjing Agric. Univ. (04), 424–431 (2014). (in Chinese)Google Scholar
  6. 6.
    Qi, Y.L., et al.: Interspecific superiority analysis of two rice series subspecies and study of rice parent selection. Henan Agric. Sci. (10), 33–36 (2005). (in Chinese)Google Scholar
  7. 7.
    Gupa, P.K.: Marker-assisted wheat breeding: present status and future possibilities. Mol. Breeding 26, 145–161 (2010)CrossRefGoogle Scholar
  8. 8.
    Guo, Z.: Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor. Appl. Genet. 124, 261–275 (2012)CrossRefGoogle Scholar
  9. 9.
    Yang, J.J., Jin, C.X., Ma, H.C.: Consideration of traditional cross-breeding parent selection factors and its application of modern breeding techniques. Gansu Agric. Sci. Technol. (01), 61–64 (2015). (in Chinese)Google Scholar
  10. 10.
    Chen, C.M.: Information Visualization: Beyond the Horizon, pp. 10–25. Springer, London (2004)Google Scholar
  11. 11.
    Yu, H.M., Liang, Z.P.: Visual data exploration and its applications. Inf. Sci. (04), 599–603 (2007). (in Chinese)Google Scholar
  12. 12.
    Zhao, R.: Design and implementation of decision tree classifier based on WEKA. Central South University (2007). (in Chinese)Google Scholar
  13. 13.
    Hu, Y., Miao, D.Q., Wang, R.Z.: A biclustering algorithm based on rough K-means. Comput. Sci. 34(11), 174–177 (2007). (in Chinese)Google Scholar
  14. 14.
    Getz, G., Levine, E., Domany, E.: Coupled two-way clustering analysis of gene microarray data. Proc. Natl. Acad. Sci. U.S.A. 97(22), 12079–12084 (2000)CrossRefGoogle Scholar
  15. 15.
    Yang, J., Wang, W., Wang, H., et al.: δ-clusters: capturing subspace correlation in a large data set. In: Proceedings of the 18th IEEE International Conference on Data Engineering (2002)Google Scholar
  16. 16.
    Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18(Suppl 1), S136–S144 (2002)CrossRefGoogle Scholar
  17. 17.
    Kluger, Y., Basri, R., Chang, J.T., et al.: Spectral biclustering of microarray data: coclustering genes and conditions. Genome Res. (13), 703–716 (2003)CrossRefGoogle Scholar
  18. 18.
    Cano, C., Adarve, L., Lopez, J., et al.: Possibilistic approach for biclustering microarray data. Comput. Biol. Med. 37(10), 1426–1436 (2007)CrossRefGoogle Scholar
  19. 19.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn, p. 150. Morgan Kaufmann Publishers, Burlington (2016)Google Scholar
  20. 20.
    Gowen, C.M., Fong, S.: Phenome analysis of microorganisms. In: Edwards, D., Stajich, J., Hansen, D. (eds.) Bioinformatics Tools and Applications. Springer, New York (2009). Scholar
  21. 21.
    Li, H., Wei, X.L.: Phenomics: a science of unravelling the genotype-phenotype relationship. Biotechnol. Bull. 7, 41–47 (2013). (in Chinese)Google Scholar

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

Personalised recommendations