**4. Conclusions**

This paper constructs a crack detection system for the electrical characteristics of eggs . With the help of a size recognition device, which automatically adapts the upper electrode position to the size of the egg, the system gives a more detailed and consistent view of the egg's surface, resulting in a more representative collected signal. Given the limitations of disorder, mutation, nonlinearity, and time discontinuity of microcurrent signals, the reliability of signal features dominates the performance of the ultimate classification model. This paper suggests an electrical-based nondestructive detection model for microcracks in poultry eggs, which employs wavelet scattering transform to extract features. Wavelet scattering transform can effectively avoid the loss of valid information and produce a signal representation insensitive to small changes in the input signal. This paper discusses the feature extraction mechanism of wavelet scattering by visualizing the output results of the scattering feature process. Finally, the study feeds the acquired feature vector into the deep learning network for classification. The following are the conclusions that can be drawn from our experiments. The microcurrent signal has unpredictable and sudden transient characteristics. The wavelet scattering transform utilized to extract signal features and develop the corresponding matrix shows a distinguished capacity to collect signals with apparent differentiation and ensure satisfactory results. In this paper, we implement this feature extraction approach combined with appropriate classifiers to discuss the classification of egg microcurrent signals. The results show that WST+1DCNN has the best performance, and the average *ACC*, *P*, *R*, *F*1, and *MCC* obtained are 99.4393%, 99.2523%, 99.6226%, 99.4357%, and 98.8819%, respectively. In addition, we set the eggs' class-imbalanced dataset and the duck egg dataset to verify the performance and universality. Finally, we conduct experiments on egg detection at different voltages. The novel feature extraction and detection method proposed in this paper can reduce the sensing voltage from 1500 V to 500 V and obtain higher detection accuracy on lower signal-to-noise data, dramatically reducing the risk of damage to hatching eggs from high-voltage electricity. In the future, the main direction of our research is how to achieve higher precision in egg crack detection under lower-voltage conditions, which specifically includes the following several aspects. The first is how to improve the shape of the brush so that it can cover a larger area of the eggshell during rotation and reduce the amount of missed area. An increase in the contact region means that we can realize the distinction at lower voltage, as it can also obtain enough current accumulation values in the crack regions. Secondly, at the algorithm level, we hope to extract more abundant and high-dimensional current features in the crack region and improve the existing algorithm to make it more representative. Finally, multi-sensor fusion is also one of our directions. We speculate that

an algorithm based on current features combined with image features or acoustic features can have higher accuracy than an algorithm based on single-current features.

**Author Contributions:** Conceptualization, C.S. and C.Z. (Changsheng Zhu); methodology, C.S., C.Z. (Changsheng Zhu) and Y.C.; software, Y.C., Y.W. and C.Z. (Changsheng Zhu); validation, C.S., C.Z. (Changsheng Zhu), C.Z. (Chun Zhang) , J.Y. and Y.C.; formal analysis, C.S., C.Z. (Chun Zhang), J.Y., Y.C., Y.W. and X.J.; investigation, C.S., Y.C., X.J. and Y.W.; resources, C.S. and J.Y.; data curation, Y.C., Y.W. and X.J.; writing—original draft preparation, C.S., C.Z. (Changsheng Zhu), Y.C. and Y.W.; writing—review and editing, C.S., C.Z. (Chun Zhang), C.Z. (Changsheng Zhu), Y.C., Y.W., X.J. and J.Y.; visualization, Y.C., Y.W. and X.J.; supervision, C.S., C.Z. (Chun Zhang), C.Z. (Changsheng Zhu) and J.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Tai'an Science and Technology Innovation Development Plan (No. 2021GX050 and No. 2020GX055), by the National Natural Science Foundation of China (52275262).

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on demand from the corresponding author at (cs.zhu@sdust.edu.cn).

**Conflicts of Interest:** The authors declare no conflict of interest.
