Honey.AI automatizes the tedious work of pollen analysis for honey’s floral source authentication with higher accuracy, using artificial intelligence and robotized low-cost microscopy. The solution standardizes the pollen counting measurement, reduces time, allow on-site real-time measurement, increases reproducibility of results, and immensely reduces human dependency.
Currently, the analysis of the pollen content in honey (called Melissopalynology) is carried out by an expert specialist who, after the sample preparation, places a little drop of centrifuged honey in a glass slide and manually scans the slide’s working area with a light microscope at x400 to detect, identify and count every pollen grain present in the sample. This method has several shortcomings; it is complex (requires a highly-trained expert with adequate skills and experience to identify different pollen morphologies), time-consuming (long-time of analysis that requires around 1-1.5 hours per sample), poorly reproducible (results depends on technician’s skills and experience, and suffers of human error) and expensive (analysis is often externalized to accredited laboratories at a price ranging of 40-120€ per analysis, which additionally, do not allow real-time results).
agROBOfood builds the European ecosystem for the effective adoption of robotics technologies in the European agri-food sector and accelerates the digital transformation to make the European agri-food sector more efficient and competitive.
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 825395.
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