Image Analysis Method and System for Honey Bee Movement Behavior Monitoring in Beehive
Date Issued
2016
Date
2016
Author(s)
Tsai, Ching-Wei
Abstract
Honey bees (Apis mellifera) are important pollinators in nature. In 2006~2007, North America occurred serious CCD (colony collapse disorder) problem. Abnormal and inadequate pollination cause enormous economic and agricultural loss, the reason of CCD problem is necessary to study. Base on the problem, we redevelop an image monitoring system in the beehive, it can provide larger FOV (field of view) and more accurate trajectory information in the beehive than former design. Provide a scientific tool to study the reason of CCD problem. In order to capture individual honey bee information in beehive, we label a water-proof text tag on each bee. After labeling, Hough circle transform technique is applied to detect tag position and to segment out the tag image. In order to extract the tag character feature, tag images are transformed into feature vectors by histogram of oriented gradient (HOG) algorithm. Extracting feature by HOG leads to feature dimension increase which slows down the computational speed. Hence, principle component of analysis (PCA) is applied to reduce the feature dimensionality first. Then, support vector machine (SVM) algorithm is applied to train and predict the tag text. After retrieving the position and text of tag, trajectory tracking algorithm is applied to generate trajectories. We establish a trajectory analysis method and activity indices are built up for evaluating honey bee’s activity in the beehive. Analyze the behavior differences of honey bee with different division of labor or different age. We design 3 experiments to verify our image system and analysis method, first part of experiment is honey bee activity comparison experiment by different labeling method. According to the experiment result, it is clear that the labeling by vacuum fixation method can keep honey bee higher activity ability than freezing method. The second part of experiment is the foraging bee and in-hive bee behavior comparison experiment. We can compare that different task of honey bee have different moving speed and activity cycle from the experiment result. The last part of experiment is different age of honey bee behavior comparison experiment, we can find out the motion pattern ratio is different from the age of honey bee and the activity indices are also different with different age of honey bee.
Subjects
Movement behavior analysis
Honey bee labeling
Character recognition
Machine Learning
Image Processing
Type
thesis
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