Agricultural Recommendation System for Crops Using Different Machine Learning Regression Methods
Sachi Nandan Mohanty and
Alok Kumar Jagadev
Additional contact information
Mamata Garanayak: School of Computer Engineering, KIIT University (Deemed), Bhubaneswar, India
Goutam Sahu: Department of Computer Science and Engineering, Centurion University of Technology and Management, Bhubaneswar, India
Sachi Nandan Mohanty: Department of Computer Engineering, College of Engineering Pune, Pune, India
Alok Kumar Jagadev: School of Computer Engineering, KIIT University (Deemed), Bhubaneswar, India
International Journal of Agricultural and Environmental Information Systems (IJAEIS), 2021, vol. 12, issue 1, 1-20
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%.
References: View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... /IJAEIS.20210101.oa1 (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:igg:jaeis0:v:12:y:2021:i:1:p:1-20
Access Statistics for this article
International Journal of Agricultural and Environmental Information Systems (IJAEIS) is currently edited by Frederic Andres
More articles in International Journal of Agricultural and Environmental Information Systems (IJAEIS) from IGI Global
Bibliographic data for series maintained by Journal Editor ().