Implementing a Computer Vision System for Corn Kernel Damage Evaluation Bern, Carl Misra, Manjit Hurburgh, Charles Hurburgh, Charles
dc.contributor.department Agricultural and Biosystems Engineering 2018-02-13T15:52:26.000 2020-06-29T22:39:28Z 2020-06-29T22:39:28Z Mon Jan 01 00:00:00 UTC 2001 2013-10-16 2001-01-01
dc.description.abstract <p>A computer vision system was developed for evaluation of the total damage factor used in corn grading. Major categories of corn damage in the Midwestern U.S. grain market were blue–eye mold damage and germ damage. Seven hundred twenty kernels were obtained from officially sampled Federal Grain Inspection Service (FGIS) corn samples and classified by inspectors on the Board of Appeals and Review. Inspectors classified these kernels into blue–eye mold, germ–damaged, and sound kernels at an 88% agreement rate. A color vision system and lighting chamber were developed to capture replicate images from each sample kernel. Images were segmented via input of red, green, and blue (RGB) values into a neural network trained to recognize color patterns of blue–eye mold, germ damage, sound germ, shadow in sound germ, hard starch, and soft starch. Morphological features (area and number of occurrences) from each of these color group areas were input to a genetic–based probabilistic neural network for computer vision image classification of kernels into blue–eye mold, germ damage, and sound categories. Correct classification by the network on unseen images was 78, 94, and 93%, respectively. Correct classification for sound and damaged categories on unseen images was 92 and 93%, respectively.</p>
dc.description.comments <p>This article is from <em>Applied Engineering in Agriculture</em> 17 (2001): 235–240. Posted with permission.</p>
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dc.identifier archive/
dc.identifier.articleid 1695
dc.identifier.contextkey 4728372
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_pubs/394
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 23:55:58 UTC 2022
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords Grain damage
dc.subject.keywords Evaluation
dc.subject.keywords Machine vision
dc.subject.keywords Probabilistic
dc.subject.keywords Neural network
dc.subject.keywords Corn
dc.subject.keywords Color
dc.subject.keywords Pattern recognition
dc.title Implementing a Computer Vision System for Corn Kernel Damage Evaluation
dc.type article
dc.type.genre article
dspace.entity.type Publication
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