A Turkish academic research team has launched an advanced technological project aimed at accelerating the pace of honey inspection and detecting commercial fraud and adulteration by integrating computer vision and artificial intelligence techniques with automated microscopy systems. The project is named "AutoPol: Otomatize Polen Tanımlama ve Flora Takip Sistemi Geliştirilmesi" and is being implemented through a joint collaboration involving Middle East Technical University (ODTÜ), Ankara University, and Hacettepe University, with support from the Scientific and Technological Research Council of Turkey (TÜBİTAK) under the 1001 program.
The new system relies on placing a prepared honey sample inside an automated microscopy system equipped with scanning and autofocus capabilities. AI-powered algorithms and digital image processing software mechanically scan the sample under the lenses without direct human intervention to generate a detailed report that accurately identifies the types and quantities of pollen present, allowing for the verification of the product's claimed botanical origin in record time compared to traditional manual methods.
What does the system change in practice?
The most prominent practical value of this innovation lies in overcoming the total reliance on manual human inspection, which requires long hours of work from experts and specialists to analyze a single sample. By fully automating this process, private laboratories and government regulatory bodies can inspect honey samples quickly and decisively, increasing the efficiency of commercial fraud monitoring processes in markets and ensuring that products comply with their declared botanical standards.
Building the first national digital pollen bank
In addition to detecting honey quality, researchers aim through the project to establish Turkey's first national digital bank for data related to pollen. This digital repository will contain databases including images and accurate scientific information for dozens of plant species and laboratory preparations. This digital system is planned to serve advanced scientific research related to biodiversity studies and monitoring environmental changes, as well as expanding the scope of application in the future to include the analysis of airborne pollen to monitor air quality and address challenges related to allergy and allergic diseases.