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

Authors

  • MV Mawale Dept of Computer Science. Adarsha Science, J.B.Arts & Birla Commerce Mahavidyalaya, Amravati, India
  • VN Chavan Dept. Of Computer Science &IT, Seth Kesarimal Porwal College, Kamptee, Nagpur, India

DOI:

https://doi.org/10.26438/ijcse/v6i4.6170

Keywords:

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

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.

References

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Published

2025-11-12
CITATION
DOI: 10.26438/ijcse/v6i4.6170
Published: 2025-11-12

How to Cite

[1]
M. Mawale and V. Chavan, “Complex analysis of classified of Soil parameters and its relationship identification using PCA”, Int. J. Comp. Sci. Eng., vol. 6, no. 4, pp. 61–70, Nov. 2025.

Issue

Vol. 6 No. 4 (2018): IJCSE April Edition

Section

Research Article

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