Feature extraction for the stored-grain insect detection system based on image recognition technology

The feature extraction is a very important and difficult part for the stored-grain insect detection system based on image recognition technology. The seventeen morphological features were extracted and normalized from the binary grain-insect images. The ant colony optimization algorithm was applied to the feature extraction of the stored-grain insects, and the recognition accuracy of the z-fold cross-validation training model was acted as an important factor for the evaluation principle of the feature extraction. The algorithm extracted seven features that were composed of the optimal feature space from the 17 morphological features, such as area and perimeter. Finally, the nine species of the stored-grain insects were recognized by the support vector machine classifier, and the correct identification ratio was over 95%. The experimental results show that the feature extraction of the stored-grain insect based on ant colony optimization algorithm is practical and feasible.