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src/ (0, 2023-11-02)
src/kmeans_with_mit_function.m (1547, 2023-11-02)
src/knn_mit_function.m (2005, 2023-11-02)
src/naive_bayes_with_mit_function.m (2041, 2023-11-02)

# ml-image-plant_virus A Note on Image Representations of Plant Viruses Used in Machine Learning Abstract for the publication 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 nucleotide sequences as pixel intensities. Three popular image-based machine learning algorithms, namely k-means, k-NN, and Naive Bayes, are employed for classification. A dataset of plant virus images is constructed, and the classifiers are trained and evaluated based on their accuracy, precision, and recall. Our experimental results demonstrate the efficacy of the proposed method in distinguishing between various plant viruses, offering promising avenues for rapid and automated virus detection and classification in agricultural settings. Code for the following image-based classification algorithms are implemented: k-means, k-NN, and Naive Bayes

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