Abstract
Honey bees have crucial role in pollination across the world. This paper presents a simple, non-invasive, system for pollen bearing honey bee detection in surveillance video obtained at the entrance of a hive. The proposed system can be used as a part of a more complex system for tracking and counting of honey bees with remote pollination monitoring as a final goal. The proposed method is executed in real time on embedded systems co-located with a hive. Background subtraction, color segmentation and morphology methods are used for segmentation of honey bees. Classification in two classes, pollen bearing honey bees and honey bees that do not have pollen load, is performed using nearest mean classifier, with a simple descriptor consisting of color variance and eccentricity features. On in-house data set we achieved correct classification rate of 88.7% with 50 training images per class. We show that the obtained classification results are not far behind from the results of state-of-the-art image classification methods. That favors the proposed method, particularly having in mind that real time video transmission to remote high performance computing workstation is still an issue, and transfer of obtained parameters of pollination process is much easier.
Highlights
As much as we are interested in beekeeping for honey production, we should take care of bees as pollinators
The proposed algorithm for pollen bearing honey bee detection is implemented in Python using OpenCV and executed on Raspberry Pi
We presented a visual based system for detection of pollen bearing honey bees in video recorded at the hive entrance
Summary
As much as we are interested in beekeeping for honey production, we should take care of bees as pollinators. Much effort is invested in understanding honey bees behavior. Number of honey bees entering and leaving the , as well as their movement have been investigated for a long time, but mostly through manual data collection and randomly through time (Abou-Shaara, 2014) and (Delaplane, 2013). In (Delaplane, 2013), standard methods for estimation of strength of honey bee colonies are described. Computer assisted analysis can be used for monitoring weight and weather conditions through sensor networks, and surface area of comb in opened hive occupied by bees or brood, or pollen, can be estimated by image processing methods. In order to gain a measure of colony foraging effort without hive opening, bee flight activity is still monitored visually by human observers (Delaplane, 2013)
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