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A Formal Statistical Data Modeling for Knowledge Discovery and Prognostic Reasoning of Arecanut Crop using Data Analytics

A Formal Statistical Data Modeling for Knowledge Discovery and Prognostic Reasoning of Arecanut Crop using Data Analytics

Rithesh Pakkala Permanki Guthu, Shamantha Rai Bellipady
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 27
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781683181019|DOI: 10.4018/IJSSCI.311447
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MLA

Rithesh Pakkala Permanki Guthu, and Shamantha Rai Bellipady. "A Formal Statistical Data Modeling for Knowledge Discovery and Prognostic Reasoning of Arecanut Crop using Data Analytics." IJSSCI vol.14, no.1 2022: pp.1-27. http://doi.org/10.4018/IJSSCI.311447

APA

Rithesh Pakkala Permanki Guthu & Shamantha Rai Bellipady. (2022). A Formal Statistical Data Modeling for Knowledge Discovery and Prognostic Reasoning of Arecanut Crop using Data Analytics. International Journal of Software Science and Computational Intelligence (IJSSCI), 14(1), 1-27. http://doi.org/10.4018/IJSSCI.311447

Chicago

Rithesh Pakkala Permanki Guthu, and Shamantha Rai Bellipady. "A Formal Statistical Data Modeling for Knowledge Discovery and Prognostic Reasoning of Arecanut Crop using Data Analytics," International Journal of Software Science and Computational Intelligence (IJSSCI) 14, no.1: 1-27. http://doi.org/10.4018/IJSSCI.311447

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Abstract

The rapidly evolving agronomic conditions and the cost of investing in agriculture are significant obstacles for farmers. The production of plantation crops must be increased to improve the farmers' financial state, and thus, there is a need to identify the various factors resulting in increased productivity. The proposed research aims to build a prognostic reasoning model that identifies and analyses the various optimal features influencing survival rate, flowering time, and crop yield of the areca nut crop using a data analytics technique. The optimal features are obtained by applying chi square test on the real dataset collected from the farmers. The resultant features are evaluated using different classifiers: naïve bayes, random forest, logistic regression, and decision tree. It has been found that the random forest performs better than other classifiers for the survival rate with a prediction accuracy of 99.33% and crop yield with a prediction accuracy of 99.67%. In contrast, the logistic regression gives a good result for the flowering time with a prediction accuracy of 95.33%.

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