Towards precise recognition of pollen bearing bees by convolutional neural networks

Automatic recognition of pollen bearing bees can provide important information both for pollination monitoring and for assessing the health and strength of bee colonies, with the consequent impact on people's lives, due to the role of bees in the pollination of many plant species. In this paper, we analyse some of the Convolutional Neural Networks (CNN) methods for detection of pollen bearing bees in images obtained at hive entrance. In order to show the in uence of colour we preprocessed the dataset images. Studying the results of nine state-of-the-art CNNs, we provide a baseline for pollen bearing bees recognition based in deep learning. For some CNNs the best results were achieved with the original images. However, our experiments showed evidence that DarkNet53 and VGG16 have superior performance against the other CNNs tested, with unsharp masking preprocessed images, achieving accuracy results of 99:1% and 98:6%, respectively.info:eu-repo/semantics/publishedVersio

Recent developments on precision beekeeping: A systematic literature review

The aim of this systematic review was to point out the current state of precision beekeeping and to draw implications for future studies. Precision beekeeping is defined as an apiary management strategy based on monitoring individual bee colonies to minimize resource consumption and maximize bee productivity. This subject that has met with a growing interest from researchers in recent years because of its environmental implications. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was selected to conduct this review. The literature search was carried out in the Scopus database for articles published between 2015 and 2023, being a very recent issue. After two rounds screening and examination, 201 studies were considered to be analysed. They were classified based on the internal parameters of the hive, in turn divided by weight, internal temperature, relative humidity, flight activity, sounds and vibrations, gases, and external parameters, in turn divided by wind speed, rainfall and ambient temperature. The study also considered possible undesirable effects of the use of sensors on bees, economic aspects and applications of Geographic Information System technologies in beekeeping. Based on the review and analysis, some conclusions and further directions were put forward

An assessment of stingless beehive climate impact using multivariate recurrent neural networks

A healthy bee colony depends on various elements, including a stable habitat, a sufficient source of food, and favorable weather. This paper aims to assess the stingless beehive climate data and examine the precise short-term forecast model for hive weight output. The dataset was extracted from a single hive, for approximately 36-hours, at every seven seconds time stamp. The result represents the correlation analysis between all variables. The evaluation of root-mean-square error (RMSE), as well as the RMSE performance from various types of topologies, are tested on four different forecasting window sizes. The proposed forecast model considers seven of input vectors such as hive weight, an inside temperature, inside humidity, outside temperature, outside humidity, the dewpoint, and bee count. The various network architecture examined for minimal RMSE are long short-term memory (LSTM) and gated recurrent units (GRU). The LSTM1X50 topology was found to be the best fit while analyzing several forecasting windows sizes for the beehive weight forecast. The results obtained indicate a significant unusual symptom occurring in the stingless bee colonies, which allow beekeepers to make decisions with the main objective of improving the colony’s health and propagation

Assessing the health status of managed honeybee colonies (HEALTHY-B): a toolbox to facilitate harmonised data collection

Tools are provided to assess the health status of managed honeybee colonies by facilitating further harmonisation of data collection and reporting, design of field surveys across the European Union (EU) and analysis of data on bee health. The toolbox is based on characteristics of a healthy managed honeybee colony: an adequate size, demographic structure and behaviour; an adequate production of bee products (both in relation to the annual life cycle of the colony and the geographical location); and provision of pollination services. The attributes ‘queen presence and performance’, ‘demography of the colony’, ‘in-hive products’ and ‘disease, infection and infestation’ could be directly measured in field conditions across the EU, whereas ‘behaviour and physiology’ is mainly assessed through experimental studies. Analysing the resource providing unit, in particular land cover/use, of a honeybee colony is very important when assessing its health status, but tools are currently lacking that could be used at apiary level in field surveys across the EU. Data on ‘beekeeping management practices’ and ‘environmental drivers’ can be collected via questionnaires and available databases, respectively. The capacity to provide pollination services is regarded as an indication of a healthy colony, but it is assessed only in relation to the provision of honey because technical limitations hamper the assessment of pollination as regulating service (e.g. to pollinate wild plants) in field surveys across the EU. Integrating multiple attributes of honeybee health, for instance, via a Health Status Index, is required to support a holistic assessment. Examples are provided on how the toolbox could be used by different stakeholders. Continued interaction between the Member State organisations, the EU Reference Laboratory and EFSA is required to further validate methods and facilitate the efficient use of precise and accurate bee health data that are collected by many initiatives throughout the EU

Honey Bee Health

Over the past decade, the worldwide decline in honey bee populations has been an important issue due to its implications for beekeeping and honey production. Honey bee pathologies are continuously studied by researchers, in order to investigate the host–parasite relationship and its effect on honey bee colonies. For these reasons, the interest of the veterinary community towards this issue has increased recently, and honey bee health has also become a subject of public interest. Bacteria, such as Melissococcus plutonius and Paenibacillus larvae, microsporidia, such as Nosema apis and Nosema ceranae, fungi, such as Ascosphaera apis, mites, such as Varroa destructor, predatory wasps, including Vespa velutina, and invasive beetles, such as Aethina tumida, are “old” and “new” subjects of important veterinary interest. Recently, the role of host–pathogen interactions in bee health has been included in a multifactorial approach to the study of these insects’ health, which involves a dynamic balance among a range of threats and resources interacting at multiple levels. The aim of this Special Issue is to explore honey bee health through a series of research articles that are focused on different aspects of honey bee health at different levels, including molecular health, microbial health, population genetic health, and the interaction between invasive species that live in strict contact with honey bee populations

Foraging ecology and conservation of honeybees, bumble bees and solitary bees

This thesis contributes to two inter-related fields of research: bee conservation and bee foraging ecology. The first focuses on solitary bee ecology, identifying forage and habitat requirements along with educating the general public on aspects of solitary bee behaviour to aid in the conservation of the studied species. These results greatly improve our understanding of two rare and understudied species of solitary bee in the UK, Eucera longicornis, the long-horned bee, and Anthophora retusa, the flower potter bee, along with information on the forage requirements of a common and non-native bee species, Colletes hederae, the ivy bee, confirming its specialisation and reliance on the plant species Hedera helix, common ivy. The information provided by this research on the two rare species is currently being used by stakeholders to help conserve populations found on their land. The second focuses on how an important but understudied environmental factor, wind, influences bee foraging behaviour. Two types of common social bees, honey bees and bumblebees, that are major pollinators were studied foraging on both artificial and natural flowers, with implications for increasing our understanding of the potential future impacts of climate change on bees. Among other things, the wind research has identified a novel part of foraging behaviour much influenced by wind, hesitancy to take-off from flowers, which increases at higher wind speeds, and results in significant decreases in flower visitation rate. This thesis has contributed novel knowledge to our understanding of the foraging ecology of two rare bee species in the UK and has identified that having a wide foraging breadth does not necessarily mean a species will be common, as was found to the case for A. retsua. Foraging behaviour was also found to be influenced by the understudied environmental variable, wind, with it being found to reduce the foraging efficiency of honey bees and increase both honey bees and bumble bees hesitancy to take off. Chapter Two studied the population of the rare solitary bee, Anthophora retusa, living at Seaford Head in Sussex, one of the 5 sites it is known from in Britain. Results showed that it forages on a range of flower species, including the very common Glechoma hederacea (ground ivy). The population is small (male population size in 2019 was estimated to be around 160 individuals). Transect surveys showed that the species is restricted to a very small area, c. 30ha area within the Seaford Head reserve. Chapter Three shows that the two populations of the rare Eucera longicornis found on Gatwick Airport land had estimated populations of 300 females and 130 males in 2019, and that these populations remained approximately stable over the three years of data collection. Eucera longicornis females collected pollen predominantly from species within the Fabaceae family, which were highly abundant within the 500m surrounding the aggregations and hence are likely to be key to their success. Chapter Four confirmed that ivy, Hedera helix is the predominant floral resource for the solitary bee, Colletes hederae (ivy bee) in Sussex, with ivy comprising 98% of pollen samples collected from females. Female C. hederae activity was synchronised with ivy bloom. However, C. hederae females did collect pollen from other plant species before ivy was in peak bloom. C. hederae was the most abundant species foraging on ivy, even when honeybee hives were present in the local area. Chapter Five used artificial flowers and wind generated by fans and found that increasing wind speed caused a significant reduction (37%) in flower visits for foraging Apis mellifera. This reduction was due to an increase in ‘hesitancy’, the time to take off from a flower once a bee had finished probing. The indirect effect of flower movement had no effect on flower visitation rate. However, it did cause an increase in flight duration but this was offset by a decrease in search time once a bee was on a flower. Chapter Six found that when foraging on natural flowers of lavender and marjoram Apis mellifera flower visit rate decreased with increasing wind speed due to an increase in handling time per flower. The influence of wind speed on flower visit rate differed between lavender and marjoram, with a sharper reduction when foraging on marjoram. This was not explained by differences in flower movement speed, with flower movement not influencing flower visit rate. Chapter Seven found that when foraging on lavender, Apis mellifera flower visit rate decreased with wind speed, whereas Bombus species flower visit rate was unaffected. However, both species did experience an increase in handling time per flower with increasing wind speed. Also, the number of Bombus foragers on a patch with wind was significantly lower than on a patch where no wind was present, indicating when given the option they will choose to forage in lower wind speeds. Chapter Eight identified that hesitancy to take off increased with wind speed for both Apis mellifera and Bombus species when foraging on seven plant species in naturally varying wind conditions. Hesitancy duration in relation to wind speed did not differ between plant species. However, independently of wind speed it did. Bombus hesitancy was found to increase significantly more with increasing flower movement when compared to A. mellifera

Remote assessment of Varroa presence in honey bee colonies using vibration measurements

Honey bee colony monitoring techniques that use hive-based sensors to continuously and remotely measure a range of parameters are increasingly being published. Non-invasive surveillance methods for the identification of Varroa destructor presence and infestation levels are, however, not as well-studied. Varroa mites adversely affect honey bees in several ways, and regular monitoring of their population is critical for successful control. The work carried out in this thesis explores the use of accelerometer sensors and vibration measurements as a non-invasive Varroa detection method. The capture of honey bee vibrations associated with infections of a bee virus (Chronic bee paralysis virus (CBPV)) is also investigated, as Varroa are known to vector approximately 20 honey bee diseases and their associated variants. The answers to three main questions are sought throughout this work: 1) can accelerometers be used to detect vibrations originating from Varroa?, 2) if so, can these vibrations be used as a remote mite monitoring tool?, and 3) do observable honey bee virus symptoms produce detectable vibrations? To conduct this investigation, accelerometers were attached to a variety of substrates and linked to a camera, for simultaneous video and vibration capture, allowing the characterisation of numerous Varroa and honey bee vibrations. The waveform data was transformed into spectrogram and two-dimensional-Fourier-transform (2DFT) images, which were used as a main analysis tool for vibrational feature identification. Principal component and discriminant function analyses were implemented for the purpose of discriminating between groups of vibrational signals and for automatic detection using machine learning within long-term recordings of freshly collected, capped brood-comb. This work demonstrates that accelerometers can detect vibrations generated by minute (1-2mm, 0.42mg) mite individuals, and in the process has enabled the discovery of a novel Varroa behaviour (jolting) that produces a unique vibrational trace. Pulses of interest were carefully characterised in terms of their visible features, periodicity, strength, and time duration. These were then used as search tools for mite detection purposes. The exciting discovery of the jolting behaviour strongly suggests that Varroa can transmit functional vibrations. Continuing to investigate and understand this phenomenon may lead, amongst other things, to novel methods of mite control in the future. These explorations showcase the potential for Varroa vibration capture in remote mite monitoring, laying the groundwork for future analysis. This thesis also demonstrates the many advantages of the lesser used 2DFT image in animal vibration research, promoting its use. In relation to question 3 and the capture of vibrations associated with viral symptoms, no specific vibrational features were identified that could be linked to honey bee trembling, an observable symptom of CBPV. Nevertheless, the results of this chapter (4) promoted the use of 2DFTs in honey bee vibrational monitoring and endorsed solutions for future improvement to this analysis. The 2DFT was also successfully implemented following the discovery of a novel honey bee vibration, here coined the 'purr' (chapter 5). This work encompasses the pursuit of knowledge in the recently evolved subject of biotremology, to compliment the growing field of remote honey bee colony monitoring, and particularly that of non-invasive Varroa detection. A better understanding of both honey bee and Varroa behaviours and biology has been established, promoting the importance of vibration research in these closely entwined species. The value and scope of accelerometer use has here been strengthened through the detection of Varroa vibrations, supporting its growing application in colony monitoring

The assessment report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on pollinators, pollination and food production

The thematic assessment of pollinators, pollination and food production carried out under the auspices of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services aims to assess animal pollination as a regulating ecosystem service underpinning food production in the context of its contribution to nature’s gifts to people and supporting a good quality of life. To achieve this, it focuses on the role of native and managed pollinators, the status and trends of pollinators and pollinator-plant networks and pollination, drivers of change, impacts on human well-being, food production in response to pollination declines and deficits and the effectiveness of responses