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Honey bees have crucial role in pollination across the world. This paper presents a simple, non-invasive, system for pollen bearinghoney bee detection in surveillance video obtained at the entrance of a hive. The proposed system can be used as a part of a morecomplex system for tracking and counting of honey bees with remote pollination monitoring as a final goal. The proposed method isexecuted in real time on embedded systems co-located with a hive. Background subtraction, color segmentation and morphologymethods are used for segmentation of honey bees. Classification in two classes, pollen bearing honey bees and honey bees that donot have pollen load, is performed using nearest mean classifier, with a simple descriptor consisting of color variance andeccentricity features. On in-house data set we achieved correct classification rate of 88.7% with 50 training images per class. Weshow that the obtained classification results are not far behind from the results of state-of-the-art image classification methods. Thatfavors the proposed method, particularly having in mind that real time video transmission to remote high performance computingworkstation is still an issue, and transfer of obtained parameters of pollination process is much easier

POLLEN BEARING HONEY BEE DETECTION IN HIVE ENTRANCE VIDEO RECORDED BY REMOTE EMBEDDED SYSTEM FOR POLLINATION MONITORING