Comparative analysis of machine learning techniques for predicting production capability of crop yield

Kalpana Jain () and Naveen Choudhary ()
Additional contact information
Kalpana Jain: College of Technology and Engineering
Naveen Choudhary: College of Technology and Engineering

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 1, No 59, 583-593

Abstract: Abstract Recently, the applicability of information technology for quality productivity in agriculture has been increasing progressively. As a result of these, there has been a significant increase in the data obtained from agricultural production. The data obtained from experimental research helps in the decision-making process through machine learning and artificial intelligence. Applications of data mining steps make raw agricultural data more meaningful. Data mining analysis used in many fields brings a variety of benefits to its use in agricultural production. This paper presents a comprehensive overview of the application of information technology in agriculture associated with crop management, such as yield prediction, water management, and climate factors based on recent research. Furthermore, this paper presents a soil-based Machine learning comparative analytical framework (SMLF) to predict crop yield production. Evaluates the influence of soil characteristics and climate factors to identify the crop yield prediction class label (High, Low, Medium). Simultaneously this framework presents a comparative analysis of the benchmark classifier to trained and labels the corpus of crop data set for High, low, medium yield prediction.

Keywords: Crop management; Crop yield prediction; Machine learning; Classification; Data mining (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s13198-021-01543-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01543-8

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-021-01543-8

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().