Detection of fungal damaged popcorn using image property covariance features
Author
Yorulmaz, O.
Pearson, T. C.
Çetin, A.
Date
2012Source Title
Computers and Electronics in Agriculture
Print ISSN
0168-1699
Publisher
Elsevier
Volume
84
Pages
47 - 52
Language
English
Type
ArticleItem Usage Stats
157
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views
101
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downloads
Abstract
Covariance-matrix-based features were applied to the detection of popcorn infected by a fungus that causes a symptom called " blue-eye" . This infection of popcorn kernels causes economic losses due to the kernels' poor appearance and the frequently disagreeable flavor of the popped kernels. Images of kernels were obtained to distinguish damaged from undamaged kernels using image-processing techniques. Features for distinguishing blue-eye-damaged from undamaged popcorn kernel images were extracted from covariance matrices computed using various image pixel properties. The covariance matrices were formed using different property vectors that consisted of the image coordinate values, their intensity values and the first and second derivatives of the vertical and horizontal directions of different color channels. Support Vector Machines (SVM) were used for classification purposes. An overall recognition rate of 96.5% was achieved using these covariance based features. Relatively low false positive values of 2.4% were obtained which is important to reduce economic loss due to healthy kernels being discarded as fungal damaged. The image processing method is not computationally expensive so that it could be implemented in real-time sorting systems to separate damaged popcorn or other grains that have textural differences.
Keywords
Correlation featuresCovariance features
Fungus detection on popcorn kernels
Image processing
SVM
Color channels
Correlation features
Covariance features
Covariance matrices
Economic loss
False positive
Image coordinates
Image pixels
Image processing - methods
Image properties
Intensity values
Kernel image
Recognition rates
Second derivatives
Sorting system
Support vector machine (SVM)
SVM
Image processing
Losses
Support vector machines
Covariance matrix
Computer simulation
Correlation
Crop damage
Detection method
Fungal disease
Fungus
Image analysis
Maize
Matrix
Pixel
Variance analysis
Fungi
Nemophila
Pseudomugilidae
Permalink
http://hdl.handle.net/11693/21454Published Version (Please cite this version)
http://dx.doi.org/10.1016/j.compag.2012.02.012Collections
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