ISSN :2582-9793

A Note on Plant Virus Images for use in Machine Learning

Original Research (Published On: 31-Dec-2023 )
A Note on Plant Virus Images for use in Machine Learning
DOI : https://dx.doi.org/10.54364/AAIML.2023.11105

Senuka D. Abeysinghe

Adv. Artif. Intell. Mach. Learn., 3 (4):1825-1833

Senuka D. Abeysinghe : University of Cincinnati

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DOI: https://dx.doi.org/10.54364/AAIML.2023.11105

Article History: Received on: 03-Oct-23, Accepted on: 24-Dec-23, Published on: 31-Dec-23

Corresponding Author: Senuka D. Abeysinghe

Email: senuka.abeysinghe24@ihsd.us

Citation: Senuka D. Abeysinghe, Sarfaraz Ahmed Mohammed, Anca Ralescu (2023). A Note on Plant Virus Images for use in Machine Learning. Adv. Artif. Intell. Mach. Learn., 3 (4 ):1825-1833

          

Abstract

    

Plant viruses pose significant threats to agriculture, causing substantial economic losses and affecting food security. Traditional methods of virus detection and classification are often labor-intensive and time-consuming. In this study, we propose a novel approach to distinguish between different plant viruses using image classifiers. We convert the viral genome sequences into images using code generalization, representing nucleotides sequences as pixel intensities. Three popular machine learning algorithms applied to a dataset of plant virus images, namely k-means, k-NN, and Naive Bayes, are employed for clustering and classification. Our initial experimental results suggest that this approach is effective in distinguishing between various plant viruses, offering promising avenues for rapid and automated virus identification and classification.

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