Open Access   Article

Complex analysis of classified of Soil parameters and its relationship identification using PCA

M.V.Mawale 1 , V.N.Chavan 2

1 Dept of Computer Science. Adarsha Science,J.B.Arts & Birla Commerce Mahavidyalaya,SGBAU Amravati University, Dhamangaon Rly,India.
2 Dept. Of Computer Science &IT, Seth Kesarimal Porwal College, Kamptee, Nagpur M.S.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-4 , Page no. 61-70, Apr-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i4.6170

Online published on Apr 30, 2018

Copyright M.V.Mawale, V.N.Chavan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: M.V.Mawale, V.N.Chavan, Complex analysis of classified of Soil parameters and its relationship identification using PCA, International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.61-70, 2018.

MLA Style Citation: M.V.Mawale, V.N.Chavan "Complex analysis of classified of Soil parameters and its relationship identification using PCA." International Journal of Computer Sciences and Engineering 6.4 (2018): 61-70.

APA Style Citation: M.V.Mawale, V.N.Chavan, (2018). Complex analysis of classified of Soil parameters and its relationship identification using PCA. International Journal of Computer Sciences and Engineering, 6(4), 61-70.

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Abstract

This study was carried out to predict meaningful information from large data set of soil parameters and representation in graphical manner to make its clear understanding This analysis help in determining role of dependent variable and independent variable in the system and their relationships, their dependability for designing any prediction system. A field study is carried out to collect information for assessing soil parameter. Soil parameters analysis is done on 902 soil samples collected from KrushiVighan Kendra, Ghatkhed, Amravati. The values of C, N, P, K, Mg, C, Fe, Cu, Zn, B, Mo, Lime, Saline, CEC, Mn, OM and pH of soil sample collected for the year 2011-2012 and 2012-2013 andPrinciple Component Analysis (PCA) is used to predict these soil parameters as a dependent and independent parameter that have direct/indirect effects on productivity.

Key-Words / Index Term

complex analysis.soilparameter,Principle Component Analysis,Cu_copper, Fe_iron; ; K_potassium; Mn_manganese; OC_organic content: P_ phosphorus; Zn_zinc.

References

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