Statistical features learning to predict the crop yield in regional areas

Title

Statistical features learning to predict the crop yield in regional areas

Creator

Ramanahalli P.P.; Siddamallu H.K.; Basavaraju R.K.Y.

Description

The plethora of information presented in the form of benchmark dataset plays a significant role in analyzing and understanding the crop yield in certain regions of regional territory. The information may be presented in the form of attributes makes a prediction of crop yield in various regions of machine learning. The information considered for processing involves data cleaning initially followed by binning to reduce the missing data. The information collected is subjected to clustering of data items based on patterns of similarity, The data items that are similar in nature is fed to the system with similarity measure, which involves understanding the distance of data items from its related data item leading to hyper parameters for analyzing of information while calculating the crop yield. The information may be used to ascertain the patterns of data that exhibit similarity with nearest neighbor represented by another attribute. Thus, the research method has yielded an accuracy of 89.62% of classification for predicting the crop yield in agricultural areas of Karnataka region. 2022 Institute of Advanced Engineering and Science. All rights reserved.

Source

International Journal of Electrical and Computer Engineering, Vol-12, No. 5, pp. 5321-5329.

Date

2022-01-01

Publisher

Institute of Advanced Engineering and Science

Subject

Classification; Clustering; Computer science; Machine learning; Statistical features

Coverage

Ramanahalli P.P., School and Computer Science and Applications, REVA University, Bangalore, India; Siddamallu H.K., School of Computer Science and Applications, REVA University, Bangalore, India; Basavaraju R.K.Y., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India

Rights

All Open Access; Gold Open Access; Green Open Access

Relation

ISSN: 20888708

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.11591/ijece.v12i5.pp5321-5329

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135259782&doi=10.11591%2fijece.v12i5.pp5321-5329&partnerID=40&md5=c921de52d50fb0823a25bfdda33c122e