ObjectiveIn order to improve the accuracy of egg appearance quality detection, an egg appearance quality detection model based on CNN-SVM model was established.MethodsCombined 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.ResultsCompared 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.ConclusionThe CNN-SVM model has strong robustness and anti-noise ability, which can effectively improve the accuracy and applicability of egg appearance quality detection.
