Weed and crop discrimination with hyperspectral imaging and machine learning

dc.contributor.advisorChairperson, Graduate Committee: Joseph A. Shawen
dc.contributor.authorScherrer, Bryan Josephen
dc.date.accessioned2019-05-24T15:16:21Z
dc.date.available2019-05-24T15:16:21Z
dc.date.issued2019en
dc.description.abstractHerbicide-resistant weed biotypes are spreading across crop fields nationally and internationally and mapping them with traditional crop science methods - cloning plants and testing their resistance levels in a lab - are costly and time consuming. A segment of the field of precision agriculture is being developed to accurately and quickly map the location of herbicide-resistant and herbicide-susceptible weeds using advanced optics and computer algorithms. In our study, we collected hundreds of thousands of spectra of herbicide-resistant and herbicide-susceptible biotypes of the weeds kochia, mare's tail and lamb's quarter and of crops including barley, corn, dry pea, garbanzo, lentils, pinto bean, safflower, sugar beet at the Southern Agricultural Research Center in Huntley, Montana using a hyperspectral imager. Plants were imaged in a controlled greenhouse setting as well as in crop fields using ground-based and drone-based imaging platforms. The spectra were differentiated from one another using a feedforward neural network machine learning algorithm. Classification accuracies depended on what plants were imaged, the age of the plants and lighting conditions of the experiment. They ranged from 77% to 99% for spectra acquired on our ground-based imaging platform and from 25% to 79% on our drone- based platform.en
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/15148en
dc.language.isoenen
dc.publisherMontana State University - Bozeman, College of Engineeringen
dc.rights.holderCopyright 2018 by Bryan Joseph Scherreren
dc.subject.lcshCropsen
dc.subject.lcshWeedsen
dc.subject.lcshOpticsen
dc.subject.lcshPhotonicsen
dc.subject.lcshRemote sensingen
dc.subject.lcshMachine learningen
dc.titleWeed and crop discrimination with hyperspectral imaging and machine learningen
dc.typeThesisen
mus.data.thumbpage20en
thesis.degree.committeemembersMembers, Graduate Committee: John Sheppard; Kevin S. Repasky.en
thesis.degree.departmentElectrical & Computer Engineering.en
thesis.degree.genreThesisen
thesis.degree.nameMSen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage80en

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