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Advances in Nondestructive Technologies for External Eggshell Quality Evaluation.

Pengpeng Yu¹, Chaoping Shen², Junhui Cheng¹

¹School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.

Sensors (Basel, Switzerland)

|September 27, 2025

Summary

This summary is machine-generated.

Nondestructive testing (NDT) methods offer precise, automated evaluation of poultry eggshell quality, surpassing traditional techniques. Advanced NDT, including deep learning, significantly improves microcrack detection and strength prediction for better food safety and economic value.

Keywords:

crack detection eggshell quality nondestructive testing strength evaluation thickness measurement

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Area of Science:

Agricultural Engineering
Food Science
Materials Science

Background:

Eggshell quality is crucial for poultry industry economics, food safety, and hatchability.
Traditional eggshell assessment methods are subjective, inefficient, and destructive.
Nondestructive testing (NDT) offers precise, automated, real-time eggshell quality evaluation.

Purpose of the Study:

To systematically review state-of-the-art NDT techniques for eggshell quality assessment.
To highlight the role of deep learning and sensor fusion in improving accuracy.
To discuss challenges and propose future directions for intelligent eggshell inspection systems.

Main Methods:

Acoustic resonance
Ultrasonic imaging
Terahertz spectroscopy
Machine vision
Electrical property sensing
Deep learning and sensor fusion

Main Results:

NDT techniques enable precise, automated, and real-time evaluation of eggshell characteristics.
Deep learning and sensor fusion achieve high accuracy in microcrack detection (up to 99.4%) and shell strength prediction.
Emerging challenges include noise, signal variability, and scalability.

Conclusions:

NDT technologies are advancing eggshell quality control in the poultry industry.
Integration of explainable AI, multimodal data, and edge computing can create intelligent inspection systems.
This review serves as a reference for improving nondestructive quality control in poultry supply chains.