Egg appearance quality detection based on CNN-SVM model
[Objective]In order to improve the accuracy of egg appearance quality detection,an egg appearance quality detection model based on CNN-SVM model was established.[Methods]Combined with the adaptive feature extraction capability of CNN and the super-generalization classification capability of SVM,the features of fully connected layers were extracted by six-layer convolutional neural network structure processing,and the CNN-SVM hybrid model was adopted,instead of the traditional CNN+softmax,an egg appearance quality detection method based on CNN-SVM model was proposed.[Results]Compared with SVM model,CNN model and KNN model,CNN-SVM model had better performance in accuracy,precision,recall and F1 score,which were 97.97%,98.10%,98.10%and 98.00%respectively.KNN model had the lowest accuracy in egg appearance quality detection,and its accuracy,precision,recall and F1 fraction are 77.46%,79.44%,76.75%and 76.90%,respectively.[Conclusion]The CNN-SVM model has strong robustness and anti-noise ability,which can effectively improve the accuracy and applicability of egg appearance quality detection..
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