Semi-automatic Analysis of cells in honeybee comb images

Our environment depends on honeybees. In the face of global honey bee decline, colony strength assessment can help apiary management and provide valuable research data. Counting honey, brood, pollen, larvae, and bee cells manually and classifying them based on visual judgement and estimation is time...

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Bibliographic Details
Published in2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) pp. 1 - 4
Main Authors Rathore, Neha, Tyagi, Praveen Kumar, Agrawal, Dheeraj
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.02.2023
Subjects
Circle Hough transform
CLAHE
Computer science
Digital images
Estimation
Honeybee
Image edge detection
Image Processing
Image segmentation
Transforms
Visualization
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Summary:Our environment depends on honeybees. In the face of global honey bee decline, colony strength assessment can help apiary management and provide valuable research data. Counting honey, brood, pollen, larvae, and bee cells manually and classifying them based on visual judgement and estimation is time-consuming, error-prone, and requires a qualified inspector. Digital image processing and AI developed automated and semi-automatic solutions to make this arduous job easier. Prior to classification of cells, it is necessary to detect all the cells correctly. The objective of this study is to provide an efficient method to identify all the uncapped cells in honey comb images. The paper is organized with the discussion of problem's background and evaluation first. Further, we give a thorough review of the existing work and explore a possible application. After that, we discussed the methodology adopted in literature. Overall, our investigation is thorough and extensive.
ISSN:2688-0288
DOI:10.1109/SCEECS57921.2023.10063122