Modelling daily weight variation in honey bee hives

Arias-Calluari, Karina; Colin, Théotime; Latty, Tanya; Myerscough, Mary R.; Altmann, Eduardo G.

doi:10.1371/journal.pcbi.1010880

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…Capped brood cells (pupae) were not included. In a recent study, Arias-Calluari and colleagues [ 45 ] modelled daily weight variation in honey bee hives to develop indicators of bee health. This approach is very interesting especially considering the monitoring networks of automatic scales that measure the weight of bee hives [ 46 , 47 ].…”

Section: Discussionmentioning

confidence: 99%

Brood indicators are an early warning signal of honey bee colony loss—a simulation-based study

Groeneveld,

Odemer,

Requier

2024

PLoS ONE

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Honey bees (Apis mellifera) are exposed to multiple stressors such as pesticides, lack of forage, and diseases. It is therefore a long-standing aim to develop robust and meaningful indicators of bee vitality to assist beekeepers While established indicators often focus on expected colony winter mortality based on adult bee abundance and honey reserves at the beginning of the winter, it would be useful to have indicators that allow detection of stress effects earlier in the year to allow for adaptive management. We used the established honey bee simulation model BEEHAVE to explore the potential of different indicators such as population size, number of capped brood cells, flight activity, abundance of Varroa mites, honey stores and a brood-bee ratio. We implemented two types of stressors in our simulations: 1) parasite pressure, i.e. sub-optimal Varroa treatment by the beekeeper (hereafter referred as Biotic stress) and 2) temporal forage gaps in spring and autumn (hereafter referred as Environmental stress). Neither stressor type could be detected by bee abundance or honey reserves at the end of the first year. However, all response variables used in this study did reveal early warning signals during the course of the year. The most reliable and useful measures seem to be related to brood and the abundance of Varroa mites at the end of the year. However, while in the model we have full access to time series of variables from stressed and unstressed colonies, knowledge of these variables in the field is challenging. We discuss how our findings can nevertheless be used to develop practical early warning indicators. As a next step in the interactive development of such indicators we suggest empirical studies on the importance of the number of capped brood cells at certain times of the year on bee population vitality.

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Section: Discussionmentioning

confidence: 99%

Brood indicators are an early warning signal of honey bee colony loss—a simulation-based study

Groeneveld,

Odemer,

Requier

2024

PLoS ONE

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“…Some works have already made use of deep learning models for beekeeping. There are existing papers related to the monitoring of hives [33,34], classification of honey bee comb cells [35], and tracking pollen [36,37], among other applications [38]. However, the use of deep learning for the detection and counting of Varroa mites has not been as widely explored.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Varroa Mite Colony Infestation Level Using New Open Software Based on Deep Learning Techniques

Divasón,

Romero,

Martinez-de-Pison

et al. 2024

Sensors

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Varroa mites, scientifically identified as Varroa destructor, pose a significant threat to beekeeping and cause one of the most destructive diseases affecting honey bee populations. These parasites attach to bees, feeding on their fat tissue, weakening their immune systems, reducing their lifespans, and even causing colony collapse. They also feed during the pre-imaginal stages of the honey bee in brood cells. Given the critical role of honey bees in pollination and the global food supply, controlling Varroa mites is imperative. One of the most common methods used to evaluate the level of Varroa mite infestation in a bee colony is to count all the mites that fall onto sticky boards placed at the bottom of a colony. However, this is usually a manual process that takes a considerable amount of time. This work proposes a deep learning approach for locating and counting Varroa mites using images of the sticky boards taken by smartphone cameras. To this end, a new realistic dataset has been built: it includes images containing numerous artifacts and blurred parts, which makes the task challenging. After testing various architectures (mainly based on two-stage detectors with feature pyramid networks), combination of hyperparameters and some image enhancement techniques, we have obtained a system that achieves a mean average precision (mAP) metric of 0.9073 on the validation set.

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“…However, during the spring, in the run up to swarming, such inspections are a necessity for good beekeeping. The ability of the beekeeper to monitor the health of their colony has aided the understanding of these challenges and the use of electronic monitoring aids, such as hive scales and temperature sensors [16][17][18][19][20][21][22][23][24]. The mass of the hive provides important information on the size and activity of the colony.…”

Section: Introductionmentioning

confidence: 99%

Winter Carbon Dioxide Measurement in Honeybee Hives

Newton,

Chamberlain,

McVeigh

et al. 2024

Applied Sciences

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Sensor technologies have sufficiently advanced to provide low-cost devices that can quantify carbon dioxide levels in honeybee hives with high temporal resolution and in a small enough package for hive deployment. Recent publications have shown that summer carbon dioxide levels vary throughout the day and night over ranges that typically exceed 5000 ppm. Such dramatic changes in a measurable parameter associated with bee physiology are likely to convey information about the colony health. In this work, we present data from four UK-based hives collected through the winter of 2022/2023, with a focus on seeing if carbon dioxide can indicate when colonies are at risk of failure. These hives have been fitted with two Sensirion SCD41 photoacoustic non-dispersive infrared (NDIR) carbon dioxide sensors, one in the queen excluder, at the top of the brood box, and one in the crown board, at the top of the hive. Hive scales have been used to monitor the hive mass, and internal and external temperature sensors have been included. Embedded accelerometers in the central frame of the brood box have been used to measure vibrations. Data showed that the high daily variation in carbon dioxide continued throughout the coldest days of winter, and the vibrational data suggested that daily fanning may be responsible for restoring lower carbon dioxide levels. The process of fanning will draw in colder air to the hive at a time when the bees should be using their energy to maintain the colony temperature. Monitoring carbon dioxide may provide feedback, prompting human intervention when the colony is close to collapse, and a better understanding may contribute to discussions on future hive design.

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Modelling daily weight variation in honey bee hives

Cited by 6 publications

References 42 publications

Brood indicators are an early warning signal of honey bee colony loss—a simulation-based study

Brood indicators are an early warning signal of honey bee colony loss—a simulation-based study

Analysis of Varroa Mite Colony Infestation Level Using New Open Software Based on Deep Learning Techniques

Winter Carbon Dioxide Measurement in Honeybee Hives

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