Fast insect damage detection in wheat kernels using transmittance images

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dc.citation.epage1346en_US
dc.citation.spage1343en_US
dc.contributor.authorÇataltepe, Z.en_US
dc.contributor.authorPearson, T.en_US
dc.contributor.authorCetin, A. Enisen_US
dc.coverage.spatialBudapest, Hungary
dc.date.accessioned2016-02-08T11:53:07Z
dc.date.available2016-02-08T11:53:07Z
dc.date.issued2004-07en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 25-29 July 2004
dc.descriptionConference name: 2004 IEEE International Joint Conference on Neural Networks
dc.description.abstractWe used transmittance images and different learning algorithms to classify insect damaged and un-damaged wheat kernels. Using the histogram of the pixels of the wheat images as the feature, and the linear model as the learning algorithm, we achieved a False Positive Rate (1-specificity) of 0.12 at the True Positive Rate (sensitivity) of 0.8 and an Area Under the ROC Curve (AUC) of 0.90 ± 0.02. Combining the linear model and a Radial Basis Function Network in a committee resulted in a FP Rate of 0.09 at the TP Rate of 0.8 and an AUC of 0.93 ± 0.03.en_US
dc.identifier.doi10.1109/IJCNN.2004.1380142en_US
dc.identifier.issn1098-7576
dc.identifier.urihttp://hdl.handle.net/11693/27428
dc.language.isoEnglishen_US
dc.publisherIEEE
dc.relation.isversionofhttp://dx.doi.org/10.1109/IJCNN.2004.1380142en_US
dc.source.titleIEEE International Conference on Neural Networks - Conference Proceedingsen_US
dc.subjectInsect detectionen_US
dc.subjectLearning methodsen_US
dc.subjectTransmittance imagesen_US
dc.subjectWheat kernelsen_US
dc.subjectCorrelation methodsen_US
dc.subjectCropsen_US
dc.subjectFeature extractionen_US
dc.subjectIndependent component analysisen_US
dc.subjectInsect controlen_US
dc.subjectLearning algorithmsen_US
dc.subjectMathematical modelsen_US
dc.subjectPrincipal component analysisen_US
dc.subjectRadial basis function networksen_US
dc.subjectImage processingen_US
dc.subjectAlgorithmsen_US
dc.subjectCorrelationen_US
dc.subjectImage analysisen_US
dc.subjectMathematical modelsen_US
dc.subjectWheaten_US
dc.titleFast insect damage detection in wheat kernels using transmittance imagesen_US
dc.typeConference Paperen_US

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