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Author Delwiche, S. R. ♦ Harel, G. A.
Source CiteSeerX
Content type Text
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Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Abstract Both wheat breeder and wheat inspector must currently deal with the assessment of scab in harvested wheat by manual human inspection. We are currently developing and examining the accuracy of a semi-automated wheat scab inspection system that is based on near-infrared (NIR) reflectance (1000 to 1700 nm) of individual kernels. Our initial work revealed that, for scanning, the kernels could be oriented in just a semi-random basis, in which the rotational angle about a kernel’s long axis was arbitrary. Classification analysis has involved the application of various statistical classification techniques, including linear discriminant analysis, soft independent modeling of class analogy (SIMCA), partial least squares regression, and non-parametric (k-nearest-neighbor) classification. For the most recent year evaluated (2002), average cross-validation accuracy ranged from 82.1 % (a wavelength difference, without kernel mass, model) to 89.6 % (a k-nearest-neighbor, with kernel mass, model). Although the lower value in this range was indeed lower than that for a model using mass alone (83.8%), the corresponding accuracies of these models on a separate (fully independent) test set indicated that the spectrally based models, with accuracies in excess of 92 % were clearly better than the mass alone model. Based on test set accuracy, there were only slight differences
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study