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We present a novel system for the automatic video monitoring of honey bee foraging activity at the hive entrance. This monitoring system is built upon convolutional neural networks that perform multiple animal pose estimation without the need for marking. This precise detection of honey bee body parts is a key element of the system to provide detection of entrance and exit events at the entrance of the hive including accurate pollen detection. A detailed evaluation of the quality of the detection and a study of the effect of the parameters are presented. The complete system also integrates identification of barcode marked bees, which enables the monitoring at both aggregate and individual levels. The results obtained on multiple days of video recordings show the applicability of the approach for large-scale deployment. This is an important step forward for the understanding of complex behaviors exhibited by honey bees and the automatic assessment of colony health.
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Analysis of Honeybee Drone Activity during the Mating Season in Northwestern Argentina
Males in Hymenopteran societies are understudied in many aspects and it is assumed that they only have a reproductive function. We studied the time budget of male honey bees, drones, using multiple methods. Changes in the activities of animals provide important information on biological clocks and their health. Yet, in nature, these changes are subtle and often unobservable without the development and use of modern technology. During the spring and summer mating season, drones emerge from the hive, perform orientation flights, and search for drone congregation areas for mating. This search may lead drones to return to their colony, drift to other colonies (vectoring diseases and parasites), or simply get lost to predation. In a low percentage of cases, the search is successful, and drones mate and die. Our objective was to describe the activity of Apis mellifera drones during the mating season in Northwestern Argentina using three methods: direct observation, video recording, and radio frequency identification (RFID). The use of RFID tagging allows the tracking of a bee for 24 h but does not reveal the detailed activity of drones. We quantified the average number of drones’ departure and arrival flights and the time outside the hive. All three methods confirmed that drones were mostly active in the afternoon. We found no differences in results between those obtained by direct observation and by video recording. RFID technology enabled us to discover previously unknown drone behavior such as activity at dawn and during the morning. We also discovered that drones may stay inside the hive for many days, even after initiation of search flights (up to four days). Likewise, we observed drones to leave the hive for several days to return later (up to three days). The three methods were complementary and should be considered for the study of bee drone activity, which may be associated with the diverse factors influencing hive health.
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- PAR ID:
- 10310371
- Date Published:
- Journal Name:
- Insects
- Volume:
- 12
- Issue:
- 6
- ISSN:
- 2075-4450
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/insects12060566
