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We present a complete methodology for authenticating local bee pollen againstfraudulent samples using image processing and machine learning techniques. Theproposed standard methods do not need expensive equipment such as advancedmicroscopes and can be used for a preliminary fast rejection of unknown pollentypes. The system is able to rapidly reject the non-local pollen samples withinexpensive hardware and without the need to send the product to thelaboratory. Methods are based on the color properties of bee pollen loadsimages and the use of one-class classifiers which are appropriate to rejectunknown pollen samples when there is limited data about them. The validation ofthe method is carried out by authenticating Spanish bee pollen types.Experimentation shows that the proposed methods can obtain an overallauthentication accuracy of 94%. We finally illustrate the user interaction withthe software in some practical cases by showing the developed applicationprototype.

Standard methods for inexpensive pollen loads authentication by means of computer vision and machine learning