Abstract
Humans are interested in the knowledge of honeybee pollen composition, which depends on the local flora surrounding the beehive, due to their nutritional value and therapeutical benefits. Currently, pollen composition is manually determined by an expert palynologist counting the proportion of pollen types analyzing the pollen of the hive with an optical microscopy. This procedure is tedious and expensive for its systematic application. We present an automatic methodology to discriminate pollen loads of various genus based on texture classification. The method consists of three steps: after selection non-blurred regions of interest (ROIs) in the original image, a texture feature vector for each ROI is calculated, which is used to discriminate between pollen types. An statistical evaluation of the algorithm is provided and discussed.
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Carrión, P., Cernadas, E., Gálvez, J.F., Díaz-Losada, E. (2003). Determine the Composition of Honeybee Pollen by Texture Classification. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_19
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DOI: https://doi.org/10.1007/978-3-540-44871-6_19
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