Fast insect damage detection in wheat kernels using transmittance images
Date
2004-07
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Source Title
IEEE International Conference on Neural Networks - Conference Proceedings
Print ISSN
1098-7576
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Publisher
IEEE
Volume
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Pages
1343 - 1346
Language
English
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Abstract
We 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.
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Keywords
Insect detection , Learning methods , Transmittance images , Wheat kernels , Correlation methods , Crops , Feature extraction , Independent component analysis , Insect control , Learning algorithms , Mathematical models , Principal component analysis , Radial basis function networks , Image processing , Algorithms , Correlation , Image analysis , Mathematical models , Wheat