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dc.contributor.authorGajjar, Viraj
dc.contributor.authorLai, Ze-Hao
dc.contributor.authorKosbar, Kurt
dc.date.accessioned2019-02-08T21:54:11Z
dc.date.available2019-02-08T21:54:11Z
dc.date.issued2018-11
dc.identifier.issn0884-5123
dc.identifier.issn0074-9079
dc.identifier.urihttp://hdl.handle.net/10150/631640
dc.description.abstractThis paper introduces a method of classifying leaves using machine learning. Considerable emphasis has been put on leaf classification for use in remote sensing applications such as plant phenotyping and precision agriculture. Convolutional neural networks (CNN) have been extensively used in computer vision for image classification. However, CNN can be computationally expensive. This paper describes a method that achieves a comparable accuracy, with a lower computational burden, using a support vector machine (SVM) classifier. This method uses image processing algorithms to extract features from Hough transform and Hough Lines. These features are then integrated with those extracted from binary images, and “eigenleaves” extracted from grayscale, gradient, and different color-space images of leaves as data preprocessing for classification. The classifier is implemented on two publicly available datasets: Flavia and Swedish; and is able to achieve state-of-the-art accuracies using a SVM classifier.en_US
dc.description.sponsorshipInternational Foundation for Telemeteringen_US
dc.language.isoen_USen_US
dc.publisherInternational Foundation for Telemeteringen_US
dc.relation.urlhttp://www.telemetry.org/en_US
dc.rightsCopyright © held by the author; distribution rights International Foundation for Telemetering
dc.titleFAST CLASSIFICATION OF LEAF IMAGES FOR AGRICULTURAL REMOTE SENSING APPLICATIONSen_US
dc.contributor.departmentMissouri University of Science and Technologyen_US
dc.identifier.journalInternational Telemetering Conference Proceedingsen_US
dc.description.collectioninformationProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.
refterms.dateFOA2019-02-08T21:54:12Z


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