Calibration and characterization of a VNIR hyperspectral imager for produce monitoring

dc.contributor.advisorChairperson, Graduate Committee: Joseph A. Shawen
dc.contributor.authorLogan, Riley Donovanen
dc.contributor.otherJoseph A. Shaw was a co-author of the article, 'Measuring the polarization response of a VNIR hyperspectral imager' in the journal 'SPIE proceedings' which is contained within this thesis.en
dc.contributor.otherBryan Scherrer, Jacob Senecal, Neil S. Walton, Amy Peerlinck, John W. Sheppard, and Joseph A. Shaw were co-authors of the article, 'Hyperspectral imaging and machine learning for monitoring produce ripeness' in the journal 'SPIE proceedings' which is contained within this thesis.en
dc.date.accessioned2021-07-07T19:29:07Z
dc.date.available2021-07-07T19:29:07Z
dc.date.issued2020en
dc.description.abstractHyperspectral imaging is a powerful remote sensing tool capable of capturing rich spectral and spatial information. Although the origins of hyperspectral imaging are in terrestrial remote sensing, new applications are emerging rapidly. Owing to its non-destructive nature, hyperspectral imaging has become a useful tool for monitoring produce ripeness. This paper describes the process of characterizing and calibrating a visible near-infrared (VNIR) hyperspectral imager for obtaining accurate images of produce to be used in machine learning algorithms for analysis. In this work, many calibrations and characterization are outlined, including: a radiance calibration, the process of calculating reflectance, pixel uniformity and image stability testing, spectral characterization, illumination source analysis, and measurement of the polarization response. The images obtained by the calibrated hyperspectral imager were converted to reflectance across a spectral range of 387.12 nm to 1023.5 nm, with a spectral resolution of 2.12 nm. A convolutional neural network was used to perform age classification for Yukon Gold potatoes, bananas, and green peppers. Additionally, a genetic algorithm was used to determine the wavelengths carrying the most useful information for age classification. Experiments were run using red green blue (RGB) images, full-spectrum hyperspectral images, and the wavelengths selected by the genetic algorithm feature selection method. Preliminary data from these analyses show promising results at accurately classifying produce age. The genetic algorithm feature selection method is being used to develop a low-cost multispectral imager for use in monitoring produce in grocery stores.en
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/15936en
dc.language.isoenen
dc.publisherMontana State University - Bozeman, College of Engineeringen
dc.rights.holderCopyright 2020 by Riley Donovan Loganen
dc.subject.lcshOptical spectroscopyen
dc.subject.lcshPhotographyen
dc.subject.lcshFarm produceen
dc.subject.lcshRemote sensingen
dc.subject.lcshPolarization (Light)en
dc.subject.lcshGenetic algorithmsen
dc.titleCalibration and characterization of a VNIR hyperspectral imager for produce monitoringen
dc.typeProfessional Paperen
mus.data.thumbpage18en
thesis.degree.committeemembersMembers, Graduate Committee: Wataru Nakagawa; Charles C. Kankelborgen
thesis.degree.departmentElectrical & Computer Engineering.en
thesis.degree.genreProfessional Paperen
thesis.degree.nameMSen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage78en

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