Abstract
The model system between peanut yield and agronomy characteristics which is nonlinear, irreversible and dissipative. The objective in the study was the peanut cultivated in the different ecological regions in Shandong province, aimed to establish the new non-nonlinear model based on Self-Organizing Maps (SOM) to improve the cultivation information of peanut growth process. In the article, applying SOM network achieved the cluster between peanut yield and agronomy characteristics about 4 variables, involved in plant height, branches, full pods and peanut yield ratio. MATLAB 7 software is used to classify 60 samplings of peanut yield and agronomy characteristics. It is concluded that the SOM network can respond the complicated information classification among each peanut yield, during the analysis, the results also showed SOM method is the most suitable for peanut yield and characteristics classification, especially analysis of clusters on basis of peanut agronomy parameters, so the study can be applied on agronomy characteristics and peanut yield of the different ecological regions in Shandong province.
Chapter PDF
References
Wan, S.: Peanut Quality. China Agricultural Scientific Press, Beijing (2007)
Wan, S., et al.: The high Quality of synergism Cultivation Theory and Technology of Peanut. China Agricultural Scientific Press, Beijing (2009)
Zhang, H., Jiao, B., Li, G.: Analysis on the Relationship between Yield and Correlated Quantitative Character of Soybean. Journal of Shanxi ricultural Science 34(2), 27–29 (2006) (in Chinese)
Wu, Z., Wang, C., Zheng, Y., et al.: Analysis of characteristics and stability of peanut yield in different ecological regions of Shandong Province. Chinese Journal of Eco-Agriculture 16(6), 1439–1443 (2008) (in Chinese)
Yang, Y., Wan, S.: The grey correlation degree analysis on yield of peanut and traits in warm temperature regions. In: Li, D., Yang, S.X. (eds.) Computer and Computing Technologies in Agriculture, vol. 24, pp. 161–167 (2010)
Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1480–1497 (1990)
Vesanto, J., Alhoniemi, E.: Clustering of the Self-Organizing Map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)
Zhang, Q., et al.: Self-organizing feature map classification and ordination of Larix principisrupprechtii forest in Pangquangou Nature Reserve
Zhang, Y., et al.: Soil Classification Based on Self Organizing. Feature Mapping Neural Networks
Moshou, D., Gravalos, I., Kateris, D., Sawalhi, N., Loutridis, S.: Condition monitoring in centrifugal irrigation pumps with self-organizing feature visualization. In: EFITA/WCCA, pp. 116-124 (2011)
Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (1995)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM Toolbox for Matlab5 (2000)
Suri, G., Zhang, J.-T., Tian, S.-G., Zhang, Q.-D., Zhang, B., Cheng, J.-J., Liu, S.-J.: Application of self-organizing map to quantitative analysis of mountain meadow in the Songshan Nature Reserve of Beijing, China. Chinese Journal of Plant Ecology 34(7), 811–818 (2010)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Self-organizing map in Matlab: the SOM Toolbox
Vesanto, J., Alhoniemi, E., Himberg, J., Kiviluoto, K., Parviainen, J.: Self-Organizing Map for Data Mining in MATLAB: the SOM Toolbox. Simulation News Europe 25, 54 (1999)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Self-organizing map in Matlab: the SOM Toolbox. In: Proceedings of the Matlab DSP Conference 1999, Espoo, Finland, pp. 35–40 (November 1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 IFIP International Federation for Information Processing
About this paper
Cite this paper
Yang, Y., Ji, M. (2013). Self-Organizing Map Analysis on Peanut Yield and Agronomy Characteristics. In: Li, D., Chen, Y. (eds) Computer and Computing Technologies in Agriculture VI. CCTA 2012. IFIP Advances in Information and Communication Technology, vol 392. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36124-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-36124-1_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36123-4
Online ISBN: 978-3-642-36124-1
eBook Packages: Computer ScienceComputer Science (R0)