Predictive Model Construction for Prediction of Soil Fertility using Decision Tree Machine Learning Algorithm

Abstract

Agriculture sector is recognized as the backbone of the Indian economy that plays a crucial role in the growth of the nation’s economy. It imparts on weather and other environmental aspects. Some of the factors on which agriculture is reliant are Soil, climate, flooding, fertilizers, temperature, precipitation, crops, insecticides, and herb. The soil fertility is dependent on these factors and hence difficult to predict. However, the Agriculture sector in India is facing the severe problem of increasing crop productivity. Farmers lack the essential knowledge of nutrient content of the soil, selection of crop best suited for the soil and they also lack efficient methods for predicting crop well in advance so that appropriate methods have been used to improve crop productivity. This paper presents different Supervised Machine Learning Algorithms such as Decision tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) to predict the fertility of soil based on macro-nutrients and micro-nutrients status found in the dataset. Supervised Machine Learning algorithms are applied on the training dataset and are tested with the test dataset, and the implementation of these algorithms is done using R Tool. The performance analysis of these algorithms is done using different evaluation metrics like mean absolute error, cross-validation, and accuracy. Result analysis shows that the Decision tree is produced the best accuracy of 99% with a very less mean square error (MSE) rate.