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

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