Abstract
The fertility detection of Specific Pathogen Free (SPF) chicken embryo eggs in vaccine preparation is a challenging task due to the high similarity among six kinds of hatching embryos (weak, hemolytic, crack, infected, infertile, and fertile). This paper firstly analyzes two classification difficulties of feature similarity with subtle variations on six kinds of five- to seven-day embryos, and proposes a novel multi-feature fusion based on Deep Convolutional Neural Network (DCNN) architecture in a small dataset. To avoid overfitting, data augmentation is employed to generate enough training images after the Region of Interest (ROI) of original images are cropped. Then, all the augmented ROI images are fed into pretrained AlexNet and GoogLeNet to learn the discriminative deep features by transfer learning, respectively. After the local features of Speeded Up Robust Feature (SURF) and Histogram of Oriented Gradient (HOG) are extracted, the multi-feature fusion with deep features and local features is implemented. Finally, the Support Vector Machine (SVM) is trained with the fused features. The verified experiments show that this proposed method achieves an average classification accuracy rate of 98.4%, and that the proposed transfer learning has superior generalization and better classification performance for small-scale agricultural image samples.
Highlights
A Specific Pathogen Free (SPF) chicken embryo egg is a virus culture source widely used in the biological vaccine preparation manufacturing industry [1]
Region of Interest (ROI); Features fusion on AlexNet and GoogLeNet deep features, Speeded Up Robust Feature (SURF) and Histogram of Oriented Gradient (HOG); Support Vector classification of 6 kinds: fertile, weak, hemolytic, crack, infected & infertile) over 98.4%
Based on the preprocessing of input original images and fine-tuning AlexNet and GoogLeNet, the deep features are extracted from the two Deep Convolutional Neural Network (DCNN), and the deep features are fused with local features of SURF and HOG
Summary
A Specific Pathogen Free (SPF) chicken embryo egg is a virus culture source widely used in the biological vaccine preparation manufacturing industry [1]. Except for the live and fertile embryo, all the infertile, weak, crack, hemolytic, and infected chicken embryo eggs must be removed from the incubator to keep them vaccination-secure [2]. With the continued incubation of the cracked embryo, it eventually becomes a fully dead and infected embryo Due to these detailed and practical requirements, the feature representations and discriminations can be considered. From the literature which have been published to date, it is clear that there are few methods for delivering discriminative functions for the classification of the six categories of chicken embryos (fertile, weak, hemolytic, cracked, infected, and infertile embryos). Neural Network (DCNN) [9,10] for blood vessels, the discriminative features of embryo body images can be automatically learned, and the DCNNs have become the mainstream in image classification and other fields of image processing. It is one’s choice to apply a pretrained network with transfer learning in a small SPF chicken embryo dataset
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