A Comparison of Classifiers for Prescreening of Honeybee Brood Cells

Abstract

We report on an image classification task originated from the video observation of beehives. Biologists desire to have an automatic support to identify so called hygienic bees. For this it is important to know which brood cells are in a stadium of initial opening. To find these cells a prescreening process is necessary which classifies three types of cells. To solve this decision problem a number of classification techniques are evaluated. ROC-analysis for the given problem shows that the SVM classifier with RBF kernel outperforms linear discrimance analysis, decision trees, boosted classifiers, and other kernel functions.