Online evaluation of the Kolmogorov–Smirnov test on arbitrarily large samples

Cardoso, Douglas O.; Galeno, Thalis D.

doi:10.1016/j.jocs.2023.101959

Cited by 3 publications

(1 citation statement)

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“…Only essential components are determined through filtering after preprocessing raw agricultural data. The best traits are found using the statistical test known as the Kolmogorov-Smirnov (KS) test to discover the knowledge about the crop (7) . In this study, predictive analysis of crop yield and tree lifetime is done with the use of classifiers namely decision tree, support vector machine (SVM), and artificial neural network (ANN).…”

Section: Contributionsmentioning

confidence: 99%

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

Pakkala,

Rai,

Rai

2023

IJST

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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.

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Section: Contributionsmentioning

confidence: 99%