NEWS
In recent years, the food industry has faced increasingly complex challenges related to product safety. Bacterial contamination, foreign objects in food, and production defects are concrete risks that companies must carefully monitor. Supporting them in this mission is YOLO (You Only Look Once), an artificial intelligence technology that is transforming how food is inspected throughout the entire production chain.
What is YOLO, how does it work, and why is it only now gaining traction in the food industry?
YOLO is a computer vision algorithm developed in 2015 by Joseph Redmon and his team for object detection in images. Its key feature is that it analyzes an entire image in a single pass, simultaneously identifying objects and their positions. Unlike traditional methods that divided an image into multiple sections and analyzed each part separately, YOLO drastically reduces processing time and enables real-time recognition.
But if this technology has existed since 2015, why is it only now becoming a standard? The main reason is the evolution of hardware and algorithms. The early versions of YOLO were less accurate and required significant computing power, making widespread adoption difficult. Over time, thanks to improvements in neural networks and the increasing availability of more powerful hardware, YOLO has become more precise and accessible, finding applications in various sectors, including food safety (Redmon et al., 2016).
Minutes instead of days: yolo transforms food safety analysis
A critical aspect of food production is the presence of physical and microbiological contaminants, which can compromise product safety and lead to costly recalls. Thanks to its speed and accuracy, YOLO is now used to detect issues directly on the production line.
A concrete example is the detection of foreign objects in food. Traditionally, X-rays or manual inspections have been used to identify plastic, glass, or insects in products, but both methods have limitations. With YOLO, however, cameras installed along production lines analyze every product in real time, identifying and flagging anomalies that would escape the human eye (Kurniawan et al., 2024).
Another key application is microbiological control. Until recently, detecting bacteria such as E. coli or Salmonella required days of laboratory testing. Today, some researchers have developed YOLO-based solutions that analyze magnified images of food samples under a microscope and identify bacterial microcolonies within hours, making inspections much faster and more efficient (UC Davis, 2023).
YOLO is also used to monitor food quality over time, detecting visual defects such as mold on baked goods or imperfections on fruits and vegetables. This allows companies to ensure consistent quality levels and promptly discard defective products before they reach store shelves (Hernandez et al., 2023).
Practical Applications: companies already looking to the future
Several companies have already implemented YOLO in their production processes with excellent results. One of the most advanced players in this field is Spore.Bio, a French startup that has developed an artificial intelligence-based microbiological testing system. Using advanced optical analysis, their device detects bacterial presence in just 10 minutes, dramatically accelerating testing times compared to traditional methods (Spore.Bio, 2024).
Major food manufacturers are also experimenting with YOLO to detect foreign objects in products. Food packaging companies have installed AI-powered cameras on conveyor belts to identify non-metallic contaminants, such as plastic or glass fragments, thereby improving overall product safety (KPM Analytics, 2023).
In the fresh-cut produce sector (salads and ready-to-eat vegetables), a YOLO-based system has been successfully tested to detect small insects or unwanted residues hidden among the leaves. The technology has demonstrated 98% accuracy, outperforming traditional manual inspections and reducing contamination risks before distribution to supermarkets (Kurniawan et al., 2024).
YOLO is also being applied to monitor hygiene conditions in production environments. Some companies have developed AI surveillance systems that verify in real time whether operators are correctly wearing gloves and protective equipment, reducing the risk of accidental contamination due to human error (Ultralytics, 2023).
The future of YOLO in food safety
The adoption of YOLO in the food industry is just beginning. With the continuous improvement of artificial intelligence algorithms and integration with other technological tools, such as advanced sensors and chemical analysis systems, quality control could become even faster and more effective.
As more companies embrace this technology, we can expect a future where food products are increasingly safe and free from contamination, with a positive impact on both consumers and the industry. In an era where food safety is a global priority, tools like YOLO represent a true revolution, leveraging computer vision to protect the health of millions of people every day.
AI food-safety Sigep Vision
