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Agricultural Recommendation System for Crops Using Different Machine Learning Regression Methods

Agricultural Recommendation System for Crops Using Different Machine Learning Regression Methods

Mamata Garanayak, Goutam Sahu, Sachi Nandan Mohanty, Alok Kumar Jagadev
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 20
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781799861584|DOI: 10.4018/IJAEIS.20210101.oa1
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MLA

Garanayak, Mamata, et al. "Agricultural Recommendation System for Crops Using Different Machine Learning Regression Methods." IJAEIS vol.12, no.1 2021: pp.1-20. http://doi.org/10.4018/IJAEIS.20210101.oa1

APA

Garanayak, M., Sahu, G., Mohanty, S. N., & Jagadev, A. K. (2021). Agricultural Recommendation System for Crops Using Different Machine Learning Regression Methods. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 12(1), 1-20. http://doi.org/10.4018/IJAEIS.20210101.oa1

Chicago

Garanayak, Mamata, et al. "Agricultural Recommendation System for Crops Using Different Machine Learning Regression Methods," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 12, no.1: 1-20. http://doi.org/10.4018/IJAEIS.20210101.oa1

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Abstract

Agriculture is a foremost field within the world, and it's the backbone in the Republic of India. Agriculture has been in poor condition. The impact of temperature variations and its uncertainty has engendered the bulk of the agricultural crops to be overripe in terms of their manufacturing. A correct forecast of crop expansion is a vital character in crop forecast management. Such forecasts will hold up the federated industries for accomplishing the provision of their occupation. ML is the method of finding new models from giant information sets. Numerous regressive ways like random forest, linear regression, decision tree regression, polynomial regression, and support vector regression will be used for the aim. Area and production are among the meteorological information that's made by necessary data. This paper figures out the yield recommendation of the crop by the accurate comparison of numerous machine learning ML regressions where the overall percentage improvement over several existing methods is 3.6%.