URBANA, Ill., USA – Eggs and poultry provide important sources of protein globally, driving a major industry with large economic impacts. Challenges to hatchery operations include embryo mortality, fertility, sex determination, and eggshell characteristics. These features have a substantial impact on production, but they are difficult and time-consuming to estimate.

A University of Illinois Urbana-Champaign research team has conducted multiple studies using near infrared (NIR) and hyperspectral imagery (HSI) to evaluate chicken eggs, potentially leading to more efficient, safe, and humane production methods. They discuss their findings in a series of publications.

Their most recent study uses HSI and machine learning to predict chick embryo mortality. Previous research has shown that embryo mortality rates in hatcheries can reach more than 10%, which impacts economic viability, production efficiency, and animal welfare.

“If there is a genetic disorder or other inherent issue, some eggs don't produce healthy chicks, and the embryo dies. This poses a health hazard, as dead embryos can harbor bacteria. If we can detect and remove them early in the incubation period, we can avoid biosecurity issues,” said lead author Md. Wadud Ahmed, a doctoral student in the Department of Agricultural and Biological Engineering (ABE), part of the College of Agricultural, Consumer and Environmental Sciences and The Grainger College of Engineering at U. of I. when the research was conducted.

Hatcheries can test for mortality by shining a bright light through the egg, but this method requires time and resources.

The researchers obtained 300 chicken eggs from the U. of I. poultry farm and placed them in a commercial incubator. They used a hyperspectral camera system to acquire images before incubation and after four days in the incubator.

After incubation, the researchers could identify spectral wavelength patterns from the images of dead and alive embryos. They used those images to build machine learning models that could interpret each egg’s status based on spectral images in early incubation. They found the best performing model reached up to 97% accuracy on day 4.

In another study, they focused on determining the sex of the embryo. Currently, hatcheries cull male chicks after incubation, but early identification could prevent this.

“Male chicks are considered a byproduct because they don't lay eggs and they are not economically feasible for meat production. Around 6 billion male chicks are culled annually in the U.S., which raises serious animal welfare, economic, and biosecurity issues for the hatchery. If we can identify the embryos early, we can avoid the culling of males and use the eggs for table eggs or in food production,” Ahmed said.

Some European countries have banned the culling of male chicks, and the U.S. poultry industry is searching for technology solutions that would enable early identification of male embryos.

For this study, the researchers also obtained eggs from the U. of I. poultry farm and placed them in a commercial incubator, acquiring HSI images before and during incubation.

“For each egg, we have the hyperspectral images and we have the reference parameter, which is whether the egg produced a male or female chick. With this information, we can create a library of images and reference parameters, and we can use machine learning and explainable AI to process this information and train the model,” Ahmed said. “Then you can select an unknown egg; the system scans the egg, and the model reads the pattern based on previous experience to predict if the egg will yield a male or female chick.”

The researchers obtained 75% accuracy at day 0 (early incubation) in classifying male and female embryos.

In additional studies, they looked at other egg characteristics, including fertility, shell strength, shell thickness, and yolk ratio.

“Conventional testing methods are destructive; for example, to measure the shell strength, you need to break the eggs. Our primary focus is to develop non-destructive, cost-effective methods. With NIR and HSI, we do not need to destroy the eggs. We just need to scan them and the machine learning model will determine the desired parameter,” said Mohammed Kamruzzaman, assistant professor in ABE and corresponding author on the papers.

While regular cameras record light in three channels (red, green, and blue) to capture visible images, NIR captures bands beyond visible light to detect chemical composition. HSI records hundreds of bands across the light spectrum to yield molecular information.

To determine shell characteristics, the researchers used NIR spectroscopy, which is less expensive than HSI, but does not capture the complex molecular information required for sex and mortality determination.

If these techniques are to be implemented by the hatchery industry, the process needs to be automated, Kamruzzaman noted.

“We are working on developing a system with a robotic arm that can separate the eggs. For example, after the machine learning model identifies an egg as male or female, the arm can remove the male eggs,” he said.

“NIR and HSI technology have applications in agriculture, food, environment, and biomedicine. It’s new for the poultry industry, but the results we obtained in our research are very promising, so I think implementing it could be very useful for the industry’s processing or farm side.”

The researchers have published their NIR datasets on shell strength, shell thickness, and yolk ratio, making them freely available for other researchers to use. They plan to publish their HSI image data sets as well.

The first paper, “Non-destructive chick embryo mortality prediction at pre-incubation and early incubation using hyperspectral imaging and explainable artificial intelligence,” is published in British Poultry Science [DOI: 10.1080/00071668.2026.2620615].

The second paper, “Non-destructive pre-incubation sex determination in chicken eggs using hyperspectral imaging and machine learning,” is published in Food Control [DOI: 10.1016/j.foodcont.2025.111233].

Additional papers:

“Non-destructive measurement of eggshell strength using NIR spectroscopy and explainable artificial intelligence,” Journal of the Science of Food and Agriculture [DOI: 10.1002/jsfa.14290].

Nondestructive Prediction of Eggshell Thickness Using NIR Spectroscopy and Machine Learning with Explainable AI, ACS Food Science and Technology [DOI: 10.1021/acsfoodscitech.4c01001].

Non-destructive detection of pre-incubated chicken egg fertility using hyperspectral imaging and machine learning, Smart Agricultural Technology [DOI: 10.1016/j.atech.2025.100857].

“Hyperspectral imaging dataset for non-destructive fertility and structural evaluation of chicken eggs,” scientific data [DOI: 10.1038/s41597-026-06556-1].

Research in the College of ACES is made possible in part by Hatch funding from USDA’s National Institute of Food and Agriculture. Additional funding was provided by NIFA Award # 2023-67015-39154.