Soil Quality Analysis and Fertility Assessment to Improve the Prediction Accuracy using Machine Learning Approach

Abstract

     Soil’s physical and chemical properties plays a dominant role to assess the  soil quality and soil fertility.The fertilizers like Phosphorous, Potassium and micro-nutrients of the soil are incumbent for the development and productiveness of the soil and plants. We have to distinguish the structures, features and characteristics of soil to improve the accuracy of crop fertility prediction. Machine learning algorithms are useful to achieve the better accuracy in soil quality prediction and assessment. This paper describes the machine learning models with regression and classification technique in python and compare different supervised algorithm to evaluate and recommended suitable crop using accuracy score of soil excellence in Nainital district of Uttarakhand, India.