Random Forest Algorithm for Soil Fertility Prediction and Grading Using Machine Learning
Keerthan Kumar T G1, Shubha C2, Sushma S A3
1Keerthan Kumar T G, Assistant Professor, Siddaganga Institute of Technology, Department of Information science and Engineering, Tumakuru, Karnataka State, India.
2Shubha C, Assistant Professor, Siddaganga Institute of Technology, Department of information science and Engineering, Tumakuru, Karnataka State, India.
3Sushma S A, Assistant Professor, Siddaganga Institute of Technology, Department of information science and Engineering, Tumakuru, Karnataka State, India.
Manuscript received on October 11, 2019. | Revised Manuscript received on 23 October, 2019. | Manuscript published on November 10, 2019. | PP: 1301-1304 | Volume-9 Issue-1, November 2019. | Retrieval Number: L36091081219/2019©BEIESP | DOI: 10.35940/ijitee.L3609.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In 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.
Keywords: Crop Recommendation, Fertility Grading, Machine Learning, Prediction, Random Forest, Linear Regression
Scope of the Article: Machine Learning