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All Journal International Journal of Electrical and Computer Engineering
Ravi Kumar Yelwala Basavaraju
University of Mysore
Computer Science & IT Electrical & Electronics Engineering

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Statistical features learning to predict the crop yield in regional areas Pinaka Pani Ramanahalli; Hemanth Kollegal Siddamallu; Ravi Kumar Yelwala Basavaraju
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5321-5329

Abstract

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.
Co-Authors Hemanth Kollegal Siddamallu Pinaka Pani Ramanahalli