Genetic algorithms for Hyperspectral Range and Operator Selection Kaleita, Amy Steward, Brian Steward, Brian Kaleita, Amy Ewing, Robert Ashlock, Daniel
dc.contributor.department Agricultural and Biosystems Engineering 2018-02-13T03:37:44.000 2020-06-29T22:34:23Z 2020-06-29T22:34:23Z Sat Jan 01 00:00:00 UTC 2005 2012-12-03 2005-07-01
dc.description.abstract <p>A novel genetic algorithm was developed using mathematical operations on spectral ranges to explore spectral operator space and to discover useful mathematical range operations for relating spectral data to reference parameters. For each range, the starting wavelength and length of the range, and a mathematical range operation were selected with a genetic algorithm. Partial least squares (PLS) regression was used to develop models predicting reference variables from the range operations. Reflectance spectra from corn plant canopies were investigated, with proportion of plants (1) with visible tassels and (2) starting to shed pollen as reference data. PLS models developed using the spectral range operator framework had similar fitness than PLS models developed using the full spectrum. This range/operator framework enabled identification of those spectral ranges with most predictive capability and which mathematical operators were most effective in using that predictive capability. Detection of operator locality may have utility in sensor and algorithm design and in developing breeding stock for other algorithms.</p>
dc.description.comments <p><a href="" target="_blank">ASAE Paper No. 053063</a></p>
dc.identifier archive/
dc.identifier.articleid 1043
dc.identifier.contextkey 3507967
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_conf/44
dc.language.iso en
dc.source.bitstream archive/|||Sat Jan 15 00:16:58 UTC 2022
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords hyperspectral analysis
dc.subject.keywords remote sensing
dc.subject.keywords spectroscopy
dc.subject.keywords corn development
dc.subject.keywords evolutionary computation
dc.title Genetic algorithms for Hyperspectral Range and Operator Selection
dc.type article
dc.type.genre conference
dspace.entity.type Publication
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relation.isAuthorOfPublication ef71fa01-eb3e-4e29-ade7-bcb38f2968b0
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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