SVM Classifier of Stored-Grain Insects Based on Grid Search
The detection of the stored-grain insects based on image recognition technology is high accuracy, cost-effective, high efficiency, no pollution and less labor. The classification of the stored-grain insects was multi-feature and multi-compound degree of various insects. The classifier design was cri...
Saved in:
Published in | Advances in Computer Science and Education Applications pp. 436 - 441 |
---|---|
Main Authors | , , |
Format | Book Chapter |
Language | English |
Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
|
Series | Communications in Computer and Information Science |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | The detection of the stored-grain insects based on image recognition technology is high accuracy, cost-effective, high efficiency, no pollution and less labor. The classification of the stored-grain insects was multi-feature and multi-compound degree of various insects. The classifier design was critical for the detection system of the stored-grain insects. The optimal parameters C and g should be identified while using RBF kernels in the SVM classifier. The goal was to get the best cross-validation training model and improve the classification accuracy of the classifier. The grid search consisting of rough selection and fine selection was proposed to optimize parameters C and g by the recognition ratio of the training model. The optimal parameters were 30574 and 0.5743 after training, respectively. The fifteen species of the stored-grain insects that spoiled seriously in granary were automatically recognized by SVM classifier, and the correct identification ratio was over 94.67%. The experiment showed that it was practical and feasible. |
---|---|
ISBN: | 9783642224553 3642224555 |
ISSN: | 1865-0929 1865-0937 |
DOI: | 10.1007/978-3-642-22456-0_62 |