• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.857-862
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• 2021

Recently, rapid climate change has had a significant impact on the bee ecosystem. The decrease in the number of bees and the change in the flowering period have a huge impact on the harvesting of beekeepers. Accordingly, attention is focused on smart beekeeping, which introduces IoT technology to beekeeping. According to the characteristics of beekeeping, it is impossible to continuously observe the beehive in the hive with the naked eye, and the condition of the hive is mostly dependent on knowledge from experience. Although a system that can measure partly through sensors such as temperature/humidity change inside the hive and measurement of the amount of CO2 is applied, there is no research on measuring the movement path and amount of movement of bees inside the beehive. Part of the migration of honeybees inside the hive can provide basic information to predict the most important cleavage time in beekeeping. In this study, we propose a device that detects the movement path of bees and measures and records data entering and exiting the hive in real time. The device proposed in this study was developed according to the honeycomb standard of the existing beehive so that beekeeping farms could use it. The development method used a photodetector that can detect the movement of bees to configure 16 movement paths and to detect the movement of bees in real time. If the measured honeybee movement status is utilized, the problem of directly observing the colony with the naked eye in order not to miss the swarming time can be solved.

• KAPE Magazine
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• s.209
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• pp.16-17
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• 2014

꿀에 대한 소비자 신뢰 확보를 위한 공공기관에서의 꿀 등급제 도입 요구에 따라 축산물품질평가원에서는 '13. 12. 30(월)부터 "꿀 등급판정 시범사업"을 실시하고 있다. 시범사업 기간은 1년('13. 12. 30~'13. 12. 31)으로 양봉농가, 시행업체, 검사기관으로 연결되는 일련의 과정과 검사결과를 전산프로그램으로 연계하여 등급판정 신청부터 출고까지의 전(全) 단계를 축평원이 확인 관리한다. 또한, 등급판정 받은 꿀 제품은 생산이력 적용으로 소비자가 직접 스마트폰을 활용하여 꿀에 대한 정보를 확인할 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.137-148
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• 2024

The European honeybee (Apis mellifera) is an important pollinator threatened by colony collapse disorder (CCD), primarily due to infestation by the Varroa mite (Varroa destructor). Traditional detection methods are invasive and time-consuming, often causing additional stress to colonies. We propose a two-stage framework using the You Only Look Once version 8 (YOLOv8) model for non-destructive and rapid detection of Varroa mite infestation. The framework uses comb light images from inside the hives. In the first stage, a YOLOv8-n model detects bees and extracts individual bee images. In the second stage, a YOLOv8-cls model classifies the infestation status of each bee. Our object detection model achieved a mAP@0.5 of 0.701, and the classification model achieved an average accuracy of 91%. These results demonstrate the effectiveness of the framework as a non-destructive method for Varroa mite detection. Based on this research, we expect to provide beekeepers with an efficient tool for early detection and management of Varroa mite infestations, potentially reducing the incidence of CCD and supporting the sustainability of apiculture.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.867-874
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• 2023

Recently, many problems are occurring in the bee ecosystem due to rapid climate change. The decline in the bee population and changes in the flowering period are having a huge impact on the harvest of bee-keepers. Since it is impossible to continuously observe the beehives in the hive with the naked eye, most people rely on knowledge based on experience about the state of the hive.Therefore, interest is focused on smart beekeeping incorporating IoT technology. In particular, with regard to swarming, which is one of the most important parts of beekeeping, we know empirically that the swarming time can be determined by the sound of the queen bee, but there is no way to systematically analyze this with data.You may think that it can be done by simply recording the sound of the queen bee and analyzing it, but it does not solve various problems such as various noise issues around the hive and the inability to continuously record.In this study, we developed a system that records queen bee sounds in a real-time cloud system and analyzes sound patterns.After receiving real-time analog sound from the hive through multiple channels and converting it to digital, a sound pattern that was continuously output in the queen bee sound frequency band was discovered. By accessing the cloud system, you can monitor sounds around the hive, temperature/humidity inside the hive, weight, and internal movement data.The system developed in this study made it possible to analyze the sound patterns of the queen bee and learn about the situation inside the hive. Through this, it will be possible to predict the swarming period of bees or provide information to control the swarming period.