Fast insect damage detection in wheat kernels using transmittance images
Date
2004-07
Authors
Advisor
Instructor
Source Title
IEEE International Conference on Neural Networks - Conference Proceedings
Print ISSN
1098-7576
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
1343 - 1346
Language
English
Type
Conference Paper
Journal Title
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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