1. Honey bee lifecycle assessment and homing success in field observations with the help of visual bee monitoring technology.
- Author
-
Knäbe, Silvio, Schmidt, Katharina, Gonsior, Gundula, Faramarzi, Farnaz, and Mack, Aline
- Subjects
HONEYBEES ,BEES ,BEEHIVES ,BEEKEEPING ,LIFE cycles (Biology) ,TECHNOLOGICAL innovations ,COMPUTER vision ,COMPUTER monitors - Abstract
There is a strong desire to enhance the knowledge creation on bee health and to get a better understanding of how available feed resources contribute to it. New technologies such as automatic carry a vast potential to gain insights into these questions. As of now, they were used to detect changes in activity and pollen foraging at colony level, but have not yet been applied to generate data at the level of individual bees. One new technology to observe survival, flight duration and frequency at colony level are radio-frequency identification (RFID) chips. With their help, the homing flight behaviour of bees equipped with sensors can be observed to find out if there is an influence of a plant protection product on the orientation of the bees, thus their ability to return to their hive (OECD GD 332). Combining data about the flight activity and life cycle of individual honey bees with data at colony level from an automatic bee counter could be very insightful for a better understanding of effects and their magnitude. The companies apic.ai and EAS Ecotox are partners in the improvement of a visual bee monitoring technology in the research project OCELI (FKZ 281C307B19). Automated visual identification of individual bees could enable the inclusion of life cycle changes, homing success, flight duration and frequency as well as individual behaviour in studies where visual monitoring technology is already in use to assess other behavioural endpoints like activity, pollen collection or share of pollen foragers. As part of the project, a proof-of-concept experiment was performed in 2021, where the apic.ai monitoring systems with computer vision technology were used to observe individual foraging bees leaving and entering the hive. Queen markers (opalite plates) with different numbers and colours were attached to bees to identify them individually. For the study, bees from two colonies were marked twice at the interval of one week with a different colour. At each marking, both freshly hatched bees young and forager bees were marked. The marking was successful. Over Time there was no difference between a more experienced marking team than a less experienced team. In a follow up tunnel trial in 2022 in Germany, marking with opalite plates was conducted to determine whether chemicals have an influence on foraging start and survival of individual foragers. apic.ai monitoring systems with computer vision technology were used to observe the activity and foraging of pollen at colony level and at the level of individuals through the marked bees. The first cohort marked were experienced foragers and the second cohort in hive bees. An algorithm saved multiple images of every bee entering and leaving the hive. These images were subsequently analyzed for markers using a neural network. Picture were checked by a person to identify the colour and the number on the plate. Thus, data could be collected on: ● the first time the bee was seen leaving the hive ● every time the bee was detected to enter and leave ● every time the bee was detected bringing pollen into the hive ● the last time the bee was seen leaving the hive Survival curves and changes in foraging and recruiting behaviour were studied using this data. The first results will be presented here to display and discuss the benefits of additional insights at the level of individual bees and the potential of the data to enhance simulation models such as BEEHAVE. [ABSTRACT FROM AUTHOR]
- Published
- 2023