Egg producers worldwide may soon have access to a groundbreaking technology that promises to transform how they assess egg freshness without breaking a single shell. Two researchers at the Ondokuz Mayıs University in Samsun, Türkiye – Hasan Alp Şahin from the Hemp Research Institute and Professor Hasan Önder from the Department of Animal Science – have successfully developed a deep learning system that can determine egg freshness with remarkable accuracy using only photographic images.
By Maile Matsimela, Digital Editor at African Farming
African Farming Digital Editor Maile Matsimela met with Prof. Önder in Pretoria and was introduced to his research papers. This research was initially conducted and presented in 2022, with the formal publication process completed in early 2024.
The breakthrough study demonstrates that AI can achieve what traditional methods have long struggled to accomplish: rapid, accurate and completely non-destructive egg quality assessment. Using a sophisticated neural network architecture called ResNet, the Turkish research team achieved an impressive 91.78% accuracy rate in determining egg freshness across a 29-day storage period.
For commercial egg producers, this technology represents a significant advancement over current industry practices. Traditional freshness assessment methods, particularly the widely used Haugh unit measurement, require breaking eggs to examine their internal structure. This destructive approach means that eggs tested for quality cannot be sold, representing a direct economic loss for producers. The new photographic method preserves every egg while providing reliable freshness data.
The research methodology was elegantly simple yet scientifically rigorous. Şahin and Önder photographed 50 Leghorn hen eggs daily over 29 consecutive days using a standard digital camera mounted on a tripod under consistent lighting conditions. These 870 high-resolution images were then fed into their deep learning algorithm, which learnt to recognise subtle visual changes that occur as eggs age.
What makes this development particularly exciting for the poultry industry is its practical applicability. The system requires only a digital camera and computer processing power, making it accessible to medium- and large-scale producers without requiring expensive specialised equipment. The technology could easily integrate into existing egg grading and packaging lines, providing real-time freshness assessment as eggs move through production facilities.
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More Than Just Quality Control
The implications extend far beyond simple quality control. Accurate freshness determination helps producers optimise their inventory management, ensuring that the freshest eggs go to markets with the highest quality standards, while older eggs can be directed towards appropriate processing channels. This optimisation reduces waste and maximises revenue from each batch of eggs produced.
Prof. Önder and researcher Şahin’s work arrives at a crucial time for the global egg industry, which faces increasing pressure to meet stringent food safety regulations while maintaining profitability. In the European Union and United States, eggs must maintain specific freshness standards within 28 to 30 days of being laid. The automated system could help producers ensure compliance while reducing the labour costs associated with manual quality assessment.
The deep learning approach also offers advantages over other non-destructive methods that have been tested in recent years. Electronic nose technology, gas chromatography and spectroscopy techniques have shown promise but often require complex sample preparation and expensive equipment. The photographic method’s simplicity and lower cost make it more accessible to a broader range of producers.
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Innovating Egg Production
Looking towards the future, this technology aligns perfectly with the agriculture industry’s movement towards precision farming and Industry 4.0 principles. As farms become increasingly automated and data-driven, systems like this, which provide objective, measurable quality metrics, become invaluable tools for maintaining a competitive advantage.
The research team suggest that their method could be further refined through hybrid algorithms and expanded datasets, potentially increasing accuracy even further. They also envision integration with robotic systems that could automatically photograph, assess and sort eggs based on freshness criteria.
For farmers considering the practical implementation of such technology, the economic benefits are clear. Reduced waste, improved quality control, faster processing times and better inventory management all contribute to enhanced profitability. Additionally, the ability to provide customers with precise freshness data could become a valuable marketing advantage in increasingly quality-conscious markets.
The success of Şahin and Önder’s research demonstrates how artificial intelligence (AI) can solve real-world agricultural challenges while improving both efficiency and sustainability. As the technology continues to develop, egg producers who embrace these innovations may find themselves at a significant competitive advantage in an increasingly demanding marketplace.
This Turkish research represents more than just a technological achievement; it exemplifies how modern science can enhance traditional agricultural practices while addressing contemporary challenges in food production and quality assurance. For the global egg industry, this development signals the beginning of a new era in quality control and inventory management.
