Statistical Driven Feature Selection for Prognostic Reasoning and Insight Exploration of Areca Nut Crop using Data Analytics Approach

Permanki Guthu Rithesh Pakkala; Bellipady Shamantha Rai; Prakhyath Rai

doi:10.17485/IJST/v16i20.2508

Article

Statistical Driven Feature Selection for Prognostic Reasoning and Insight Exploration of Areca Nut Crop using Data Analytics Approach

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i20.2508

Year: 2023, Volume: 16, Issue: 20, Pages: 1495-1504

Original Article

Statistical Driven Feature Selection for Prognostic Reasoning and Insight Exploration of Areca Nut Crop using Data Analytics Approach

Permanki Guthu Rithesh Pakkala^1*, Bellipady Shamantha Rai¹, Prakhyath Rai¹

¹Sahyadri College of Engineering & Management, Mangaluru, Karnataka and Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India

*Corresponding Author
Email: [email protected]

Received Date:17 February 2023, Accepted Date:26 April 2023, Published Date:25 May 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To develop a prognostic reasoning model for discovering insights about the areca nut crop and to recommend an optimal strategy for improving crop productivity and heightening the lifetime of the areca nut tree using a statistical feature selection technique namely Kolmogorov–Smirnov (KS) test and data analytics approach. Methods: Data for the study was gathered by distributing questionnaires to farmers cultivating the areca nut crop in the Mangaluru region. Farmers can plan ahead of time to improve crop yield and estimate the lifetime of the tree with this strategy. The Kolmogorov–Smirnov test is employed for the pre-processed data, and optimal features are selected. To forecast crop yield and tree lifetime, various classifiers namely decision tree, support vector machine (SVM), and artificial neural network (ANN) are applied to 300 test samples and their performance is evaluated. Findings: The findings of the experiment show that the decision tree works better than other classifiers for crop yield and tree lifetime with a prediction accuracy of 96 % and 94.66 % respectively. Novelty: The proposed study performs the extraction of significant features of the areca nut crop using the KS test that results in the prediction of agricultural production and forecasting tree lifetime.

Keywords: Arecanut Crop; Crop Yield; Tree Lifetime; Feature Selection; Kolmogorov-Smirnov Test; Data Analytics; Prognostic Reasoning Model

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Copyright

© 2023 Pakkala et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)