Nondestructive Detection of Microcracks in Poultry Eggs Based on the Electrical Characteristics Model

Chenbo Shi, Yuxin Wang, Chun Zhang, Jin Yuan, Yanhong Cheng, Baodun Jia and Changsheng Zhu
Additional contact information
Chenbo Shi: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Yuxin Wang: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Chun Zhang: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Jin Yuan: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
Yanhong Cheng: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Baodun Jia: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Changsheng Zhu: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China

Agriculture, 2022, vol. 12, issue 8, 1-23

Abstract: The eggshell is the major source of protection for the inside of poultry eggs from microbial contamination. Timely detection of cracked eggs is the key to improving the edible rate of fresh eggs, hatching rate of breeding eggs and the quality of egg products. Different from traditional detection based on acoustics and vision, this paper proposes a nondestructive method of detection for eggshell cracks based on the egg electrical characteristics model, which combines static and dynamic electrical characteristics and designs a multi-layer flexible electrode that can closely fit the eggshell surface and a rotating mechanism that takes into account different sizes of eggs. The current signals of intact eggs and cracked eggs were collected under 1500 V of DC voltage, and their time domain features (TFs), frequency domain features (FFs) and wavelet features (WFs) were extracted. Machine learning algorithms such as support vector machine (SVM), linear discriminant analysis (LDA), decision tree (DT) and random forest (RF) were used for classification. The relationship between various features and classification algorithms was studied, and the effectiveness of the proposed method was verified. Finally, the method is proven to be universal and generalizable through an experiment on duck eggshell microcrack detection. The experimental results show that the proposed method can realize the detection of eggshell microcracks of less than 3 μm well, and the random forest model combining the three features mentioned above is proven to be the best, with a detection accuracy of cracked eggs and intact eggs over 99%. This nondestructive method can be employed online for egg microcrack inspection in industrial applications.

Keywords: electrical characteristics; poultry eggs; nondestructive detection; cracked eggs; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/8/1137/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/8/1137/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:8:p:1137-:d:877218

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().