Senuka D. Abeysinghe
Adv. Artif. Intell. Mach. Learn., 3 (4):1825-1833
Senuka D. Abeysinghe : University of Cincinnati
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
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.