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.
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- SVM Classifier of Stored-Grain Insects Based on Grid Search
- Springer Berlin Heidelberg
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