Open AccessRandom Forest Algorithm for Soil Fertility Prediction and Grading Using Machine Learning
Author(s) -
C Shubha,
S A Sushma
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l3609.119119
Subject(s) - random forest , soil fertility , mean squared error , grading (engineering) , agriculture , machine learning , computer science , crop yield , soil quality , algorithm , population , regression analysis , artificial intelligence , agricultural engineering , mathematics , soil science , statistics , environmental science , soil water , agronomy , engineering , geography , civil engineering , demography , archaeology , sociology , biology
n society the population is increasing at a high rate, people are not aware of the advancement of technologies. Machine learning can be used to increase the crop yield and quality of crops in the agriculture sector. In this project we propose a machine learning based solution for the analysis of the important soil properties and based on that we are dealing with the Grading of the Soil and Prediction of Crops suitable to the land. The various soil nutrient EC (Electrical Conductivity), pH (Power of Hydrogen), OC (Organic Carbon), etc. are the feature variables, whereas the grade of the particular soil based on its nutrient content is the target variable. Dataset is preprocessed and regression algorithm is applied and RMSE (Root Mean Square Error) is calculated for predicting rank of soil and we applied various Classification Algorithm for crop recommendation and found that Random Forest has the highest accuracy score.