Title: Statistical analysis of machine learning techniques for predicting powdery mildew disease in tomato plants

Authors: Anshul Bhatia; Anuradha Chug; Amit Prakash Singh

Addresses: University School of Information, Communication, and Technology, Guru Gobind Singh Indraprastha University, Dwarka, Sector-16C, New Delhi-110078, India ' University School of Information, Communication, and Technology, Guru Gobind Singh Indraprastha University, Dwarka, Sector-16C, New Delhi-110078, India ' University School of Information, Communication, and Technology, Guru Gobind Singh Indraprastha University, Dwarka, Sector-16C, New Delhi-110078, India

Abstract: Powdery mildew is a dangerous disease that reduces the quality and the yield of tomato fruit rapidly. Its early prediction is a prior requirement for obtaining good quality fruit. Therefore, in this study, the best classifier amongst various classifiers has been discovered using different machine learning algorithms. This classifier can precisely classify whether the meteorological conditions of a particular day are conducive to the development of powdery mildew disease or not. Tomato powdery mildew disease dataset has been tested using various performance measures and the results computed for all the classifiers are promising. Friedman test has been used to rank multiple classifiers and post hoc analysis has also been done using the Nemenyi test. It has been observed in comparison that 62.05% of the total pairs of classifiers perform significantly different from each other, and medium Gaussian support vector machine (MGSVM) is the best classifier with 94.74% accuracy.

Keywords: plant disease; tomato; powdery mildew; machine learning algorithm; Friedman test; Nemenyi test; classifier.

DOI: 10.1504/IJIEI.2021.116087

International Journal of Intelligent Engineering Informatics, 2021 Vol.9 No.1, pp.24 - 58

Received: 20 Apr 2020
Accepted: 14 Aug 2020

Published online: 30 Jun 2021 *

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