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dc.contributor.advisorMason, Jennifer
dc.contributor.authorMorris, Jacob
dc.date.accessioned2022-05-04T20:59:48Z
dc.date.available2022-05-04T20:59:48Z
dc.date.issued2022-05-02
dc.identifier.urihttp://hdl.handle.net/10150/664163
dc.description.abstractLocating and identification of plant stress and diseases plays a major role in plant conservation and human safety concerns relating to falling hazards and reduction in fire blocks between structures in medium sized population centers. Overall flora heath can be indicated by visual observations of the chlorophyll and other pigments in the leaves. As outside interference with the plants ability to naturally produce the required nutrients, such as environmental and pathological interference, the visible pigmentation change. In this study, pigment variation is evaluated and analyzed by machine learning methods including image classification for the evaluation of health in deciduous trees. By utilizing multispectral imagery this study compares wavelength values for identified affected individuals showing visual symptoms to located other affected individuals both showing symptomatic and non-symptomatic individuals. Data analysis was conducted utilizing a trained supervised classification, support vector machines and K nearest neighbor method to determine which methods was most precise in identifying affected pixels for fast-tracking management evaluations for resource managers. The overall classification accuracy of targeted, healthy, fields, and urban was relatively good, with kappa values ranging from 0.66 to 0.75 and overall accuracy ranging from 70%to 83%. Support vector machines accuracy of 82.13% with a kappa coefficient of 0.74 at a 750 point accuracy assessment making it the best method of the two for detections of symptomatic and asymptomatic individuals.en_US
dc.language.isoen_USen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.subjectdistressed detectionen_US
dc.subjectdisease detectionen_US
dc.subjectmachine learningen_US
dc.subjectmultispectral imagingen_US
dc.titleDETECTION OF DISTRESS AND DISEASE IN DECIDUOUS TREES UTILIZING REMOTE SENSINGen_US
dc.typeElectronic Reporten_US
dc.typetext
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGeographic Information Systems Technologyen_US
thesis.degree.nameM.S.en_US
dc.description.collectioninformationThis item is part of the MS-GIST Master's Reports collection. For more information about items in this collection, please contact the UA Campus Repository at repository@u.library.arizona.edu.en_US
refterms.dateFOA2022-05-04T20:59:50Z


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