The egg is considered one of the best sources of dietary protein, and has an important role in human growth and development. With the increase in the world's population, per capita egg consumption is also increasing. Ground‐breaking technological developments have led to numerous inventions like the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data, and cloud computing, transforming the conventional industry into a smart and sustainable egg industry, also known as Egg Industry 4.0 (EI 4.0). The EI 4.0 concept has the potential to improve automation, enhance biosecurity, promote the safeguarding of animal welfare, increase intelligent grading and quality inspection, and increase efficiency. For a sustainable Industry 4.0 transformation, it is important to analyze available technologies, the latest research, existing limitations, and prospects. This review examines the existing non‐destructive optical sensing technologies for the egg industry. It provides information and insights on the different components of EI 4.0, including emerging EI 4.0 technologies for egg production, quality inspection, and grading. Furthermore, drawbacks of current EI 4.0 technologies, potential workarounds, and future trends were critically analyzed. This review can help policymakers, industrialists, and academicians to better understand the integration of non‐destructive technologies and automation. This integration has the potential to increase productivity, improve quality control, and optimize resource management toward sustainable development of the egg industry.

Eggshells play a number of important roles in the avian and reptile kingdom: protection of internal contents and as a major source of minerals for developing embryos. However, when researching these respective roles, eggshell thickness measurement remains a bottleneck due to the lack of a non-destructive measurement techniques. As a result, many avian and reptile research protocols omit consideration of eggshell thickness bias on egg or embryo growth and development. Here, we validate a non-destructive method to estimate eggshell thickness based on terahertz (THz) reflectance spectroscopy using chicken white coloured eggs. Since terahertz waves are reflected from outer air-eggshell interface, as well as the inner eggshell-membrane boundary, the resulting interference signals depend on eggshell thickness. Thus, it is possible to estimate shell thickness from the oscillation distance in frequency-domain. A linear regression-based prediction model for non-destructive eggshell thickness measurement was developed, which had a coefficient of determination (R²) of 0.93, RMSEP of 0.009, RPD of 3.45 and RER 13.67. This model can estimate eggshell thickness to a resolution of less than 10 μm. This method has the potential to expand the protocols in the field of avian and reptile research, as well as be applied to industrial grading of eggs.

1. Sex-specific variations in early embryonic development rates may pre-empt later variations in embryonic development through to pipping and hatching. Given that erythropoiesis (blood production) can be equated with early embryonic growth rate, it was hypothesised that blood pigment haemoglobin can act as a specific spectral fingerprint for changes in growth rate. Moreover, by measuring longitudinal, rather than lateral, spectral transmission through the egg, a more consistent spectrum with a higher signal-to-noise ratio could be captured. 2. Longitudinal visible transmission (T575/T598 ratio), which is sensitive to haemoglobin, was used to monitor sex-specific early embryonic development rate in white layer chicken eggs from d 0 to 8 of incubation. The sex of these eggs was subsequently confirmed two days after hatching. 3. Embryonic development was detectable from d 3 (72 h) of incubation, 36 h earlier than previously reported lateral spectral measurements, supporting the greater sensitivity of longitudinal measurements. 4. At d 3, the mean T575/T598 ratio for male embryos was significantly lower (P<0.001) (i.e. higher absorbance of haemoglobin) than for female embryos, which was thought to be due to sex-differences in early embryogenesis. On the other hand, female embryos had a significantly lower (P<0.05) mean T575/T598 ratio than male embryos at d 7 of incubation, presumably due to the combined effects of oestrogen synthesis receptors and enzymes on erythropoiesis in female embryos at this time. 5. In conclusion, the proposed methodology has the sensitivity to differentiate sex-specific embryonic development rates during early incubation and the potentiality to advance precision incubation management and poultry research.

This study explores the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset using augmented images. The training results demonstrated that all models achieved high accuracy, indicating their ability to accurately learn and classify chicken eggs’ fertility state. However, when evaluated on the testing set, variations in accuracy and performance were observed. VGG16 achieved a high accuracy of 0.9803 on the testing set but had challenges in accurately detecting fertile eggs, as indicated by a NaN sensitivity value. ResNet50 also achieved an accuracy of 0.98 but struggled to identify fertile and non-fertile eggs, as suggested by NaN values for sensitivity and specificity. However, InceptionNet demonstrated excellent performance, with an accuracy of 0.9804, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.9615 for identifying non-fertile eggs. MobileNet achieved an accuracy of 0.9804 on the testing set; however, it faced challenges in accurately classifying the fertility status of chicken eggs, as indicated by NaN values for both sensitivity and specificity. While the models showed promise during training, variations in accuracy and performance were observed during testing. InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. Further optimization and fine-tuning of the models are necessary to address the limitations in accurately detecting fertile and non-fertile eggs. This study highlights the potential of CNN-Transfer Learning for nondestructive fertility detection and emphasizes the need for further research to enhance the models’ capabilities and ensure accurate classification.