Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Prashaant, C.; Bijeesh, T.V.; Bejoy, B.J.; Raju, G. Title A name given to the resource A Study on the Selection of Features, Classifiers, and Resampling in Plant Disease Detection from Leaf Images Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1834 LNNS;pp.297-310 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-95-7241-0_22" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-7241-0_22</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040265166?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040265166?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Prashaant C., CHRIST University, Bengaluru, India; Bijeesh T.V., CHRIST University, Bengaluru, India; Bejoy B.J., CHRIST University, Bengaluru, India; Raju G., Sahrdaya College of Engineering and Technology, Kerala, Thrissur, India Description An account of the resource Computer vision has become an integral part of modern agriculture. One of the key applications of computer vision is the automatic detection and classification of plant disease from digital images of plant leaves. In this study, we evaluate the discriminatory capability of selected texture features in identifying plant diseases from leaf images. Texture features are extracted from resized raw images. Experiments are carried out with public data sets of five different plants. Through extensive experimentation, two classifiersRandom forest and XGBoost are chosen for the evaluation. The class imbalance problem is addressed with a simple resampling. Resampling considerably improves the prediction accuracy. With the raw input images, the best feature as well as classifier depends on the plant type and the quality of the input images. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Machine learning; Plant diseases; Texture features Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981957240-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online