Predicting and Estimating the Major Nutrients of Soil Using Machine Learning Techniques

The prediction of soil properties leads to a better understanding of the soil ecosystem dynamics and effective soil management practices which will eventually lead to sustainable agricultural and environmental management. Using machine learning, the classification of soil will be easy and efficient. This paper presents an approach to design simple linear regression and multiple linear regression-based models to analyze basic soil macronutrients and micronutrients. The models were used to check the interdependency of the most important nutrients, namely, nitrogen (N), phosphorus (P), and potassium (K), also known as primary nutrients of soils, and also to evaluate the impact of N content over other vital soil nutrients. The results obtained by these three models justified the interdependency of these vital nutrients over each other.