Soil Properties Prediction for Agriculture using Machine Learning Techniques
by Vijay Kumar* 
, Jai Singh Malhotra, Saurav Sharma, Parth Bhardwaj
1 Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, 177005, India
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 3, Page # 09-18, 2022; DOI: 10.55708/js0103002
Keywords: Machine Learning, Agriculture, Prediction, Soil Properties
Received: 08 January 2022, Revised: 14 February 2022, Accepted: 26 February 2022, Published Online: 17 March 2022
AMA Style
Kumar V, Malhotra JS, Sharma S, Bhardwaj P. Soil Properties Prediction for agriculture using Machine Learning Techniques. Journal of Engineering Research and Sciences. 2022;1(3):9-18. doi:10.55708/js0103002
Chicago/Turabian Style
Kumar, Vijay, Jai Singh Malhotra, Saurav Sharma, and Parth Bhardwaj. “Soil Properties Prediction for Agriculture Using Machine Learning Techniques.” Journal of Engineering Research and Sciences 1, no. 3 (2022): 9–18. https://doi.org/10.55708/js0103002.
IEEE Style
V. Kumar, J. S. Malhotra, S. Sharma, and P. Bhardwaj, “Soil Properties Prediction for agriculture using Machine Learning Techniques,” Journal of Engineering Research and Sciences, vol. 1, no. 3, pp. 9–18, 2022.
information about soil properties help the farmers to do effective and efficient farming, and yield mo . An attempt has been made in this paper to predict the soil properties using machine learning approaches. The main properties of soil prediction are Calcium, Phosphorus, pH, Soil Organic Carbon, and Sand. These properties greatly affect the production of crops. Four well-known machine learning models, namely, multiple linear regression, random forest regression, support vector machine, and gradient boosting, are used for prediction of these soil properties. The performance of these models is evaluated on Africa Soil Property Prediction dataset. Experimental results reveal that the gradient boosting outperforms the other models in terms of coefficient of determination. Gradient boosting is able to predict all the soil properties accurately except phosphorus. It will be helpful for the farmers to know the properties of the soil in their particular terrain.
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