Prediction of Soil Macronutrients Using Machine Learning Algorithm

  • Umm E Farwa Smart City Lab – NCAI, Computer & Information Systems Engineering Department, University Road Gulshan-e-Iqbal, Karachi 74600, Pakistan
  • Ahsan Ur Rehman Smart City Lab – NCAI, Computer & Information Systems Engineering Department, University Road Gulshan-e-Iqbal, Karachi 74600, Pakistan
  • Saad Qasim Khan Computer & Information Systems Engineering Department, University Road, Gulshan-e-Iqbal, Karachi 74600, Pakistan
  • Muhammad Khurram Computer & Information Systems Engineering Department, University Road, Gulshan-e-Iqbal, Karachi 74600, Pakistan
Keywords: Machine Learning(ML), Cation Exchange Capacity(CEC), Linear Regression, Ridge Regression, Bayesian Regression, NPK, Agriculture, Soil Nutrients, Soil pH, Nitrogen, Phosphorus, Potassium

Abstract

In this research work, machine learning algorithms were applied to find the relationship between independent variables and dependent variables for soil data analysis. The independent variables include moisture, temperature, soil pH, Cation Exchange Capacity(CEC) whereas, the dependent variables include Nitrogen, Phosphorus and Potassium (NPK). This research concludes relationships between Phosphorus, Potassium,  soil pH and CEC; Nitrogen and soil moisture and temperature using machine learning(ML) algorithms so as to deduce NPK content of soil. A comparative analysis with obtained results from each ML method is also presented. Machine learning algorithms are best performed on data with multiple independent variables. The values computed for nitrogen relationship were more accurate than PK relationship values. The accuracy of data set I was less than data set II. A large data set would produce more accurate results for both data sets.

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Published
2020-04-08
How to Cite
Umm E Farwa, Ahsan Ur Rehman, Qasim Khan, S., & Khurram, M. (2020). Prediction of Soil Macronutrients Using Machine Learning Algorithm. International Journal of Computer (IJC), 38(1), 1-14. Retrieved from https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1527
Section
Articles