Prediction of Soil Macronutrients Using Machine Learning Algorithm
Keywords:Machine Learning(ML), Cation Exchange Capacity(CEC), Linear Regression, Ridge Regression, Bayesian Regression, NPK, Agriculture, Soil Nutrients, Soil pH, Nitrogen, Phosphorus, Potassium
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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