Identification of the honey bee swarming process by analysing the time course of hive vibrations

Honey bees live in groups of approximately 40,000 individuals and go through their reproductive cycle by the swarming process, during which the old queen leaves the nest with numerous workers and drones to form a new colony. In the spring time, many clues can be seen in the hive, which sometimes demonstrate the proximity to swarming, such as the presence of more or less mature queen cells. In spite of this the actual date and time of swarming cannot be predicted accurately, as we still need to better understand this important physiological event. Here we show that, by means of a simple transducer secured to the outside wall of a hive, a set of statistically independent instantaneous vibration signals of honey bees can be identified and monitored in time using a fully automated and non-invasive method. The amplitudes of the independent signals form a multi-dimensional time-varying vector which was logged continuously for eight months. We found that combined with specifically tailored weighting factors, this vector provides a signature highly specific to the swarming process and its build up in time, thereby shedding new light on it and allowing its prediction several days in advance. The output of our monitoring method could be used to provide other signatures highly specific to other physiological processes in honey bees, and applied to better understand health issues recently encountered by pollinators

Developing an AI-based Integrated System for Bee Health Evaluation

Honey bees pollinate about one-third of the world's food supply, but bee colonies have alarmingly declined by nearly 40% over the past decade due to several factors, including pesticides and pests. Traditional methods for monitoring beehives, such as human inspection, are subjective, disruptive, and time-consuming. To overcome these limitations, artificial intelligence has been used to assess beehive health. However, previous studies have lacked an end-to-end solution and primarily relied on data from a single source, either bee images or sounds. This study introduces a comprehensive system consisting of bee object detection and health evaluation. Additionally, it utilized a combination of visual and audio signals to analyze bee behaviors. An Attention-based Multimodal Neural Network (AMNN) was developed to adaptively focus on key features from each type of signal for accurate bee health assessment. The AMNN achieved an overall accuracy of 92.61%, surpassing eight existing single-signal Convolutional Neural Networks and Recurrent Neural Networks. It outperformed the best image-based model by 32.51% and the top sound-based model by 13.98% while maintaining efficient processing times. Furthermore, it improved prediction robustness, attaining an F1-score higher than 90% across all four evaluated health conditions. The study also shows that audio signals are more reliable than images for assessing bee health. By seamlessly integrating AMNN with image and sound data in a comprehensive bee health monitoring system, this approach provides a more efficient and non-invasive solution for the early detection of bee diseases and the preservation of bee colonies

Audio-Based Identification of Queen Bee Presence Inside Beehives

Honeybees are essential for the health of people and the planet. They play a key role in the pollination of most crops. The high mortality observed in the last decade, caused by stress factors among which the climate change, have raised the necessity of remote sensing the beehives to help monitor the health of honeybees and better understand this phenomenon. Several solutions have been proposed in the literature, and some of them include the analysis of in-hive sounds. In this scenario, we explore the potential of machine learning methods for queen bee detection using only the audio signal, being a good indicator of the colony state of health. In particular, we experiment support vector machines and neural network classifiers. We consider the effect of varying the audio chunk duration and the adoption of different hyperparameters

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

Dynamic noise filtering for multi-class classification of beehive audio data

Honeybees are the most specialized insect pollinators and are critical not only for honey production but, also, for keeping the environmental balance by pollinating the flowers of a wide variety of crops.Recording and analyzing bee sounds became a fundamental part of recent initiatives in the development of so-called smart hives. The majority of researches on beehive sound analytics are focusing on swarming detection, a relatively simple binary classification task (due to the obvious difference in the sound of a swarming and a non-swarming bee colony) where machine learning models achieve good performance even when trained on small data.However, in the case of more complex tasks of beehive sound analytics, even modern machine learning approaches perform poorly. First, training such models would need a large dataset but, according to our knowledge, there is no publicly available large-scale beehive audio data. Second, due to the specifics of beehive sounds, efficient noise filtering methods would be required, however, we could not find a noise filtering method that would increase the performance of machine learning models substantially.In this paper, we propose a dynamic noise filtering method applicable on spectrograms (image representations of audio data) which is superior to the most popular image noise filtering baselines. Further, we introduce a multi-class classification task of bee sounds and a large-scale dataset consisting of 10.000 beehive audio recordings. Finally, we provide the results of a large-scale experiment involving various combinations of audio feature extraction and noise filtering methods together with various deep learning models. We believe that the contributions of this paper will facilitate further research in the area of (beehive) sound analytics

MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees

We present a longitudinal multi-sensor dataset collected from honey bee colonies (Apis mellifera) with rich phenotypic measurements. Data were continuously collected between May-2020 and April-2021 from 53 hives located at two apiaries in Qu\'ebec, Canada. The sensor data included audio features, temperature, and relative humidity. The phenotypic measurements contained beehive population, number of brood cells (eggs, larva and pupa), Varroa destructor infestation levels, defensive and hygienic behaviors, honey yield, and winter mortality. Our study is amongst the first to provide a wide variety of phenotypic trait measurements annotated by apicultural science experts, which facilitate a broader scope of analysis. We first summarize the data collection procedure, sensor data pre-processing steps, and data composition. We then provide an overview of the phenotypic data distribution as well as a visualization of the sensor data patterns. Lastly, we showcase several hive monitoring applications based on sensor data analysis and machine learning, such as winter mortality prediction, hive population estimation, and the presence of an active and laying queen.Comment: Under review; project webpage: https://zhu00121.github.io/MSPB-webpage

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

The effects of methyl parathion on the colony dynamics of Apis mellifera : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Ecology at Massey University, Palmerston North, New Zealand

The detrimental effects of pesticides to honey bee colonies were assessed using a combination of electronic and manual sampling techniques. Initial experiments determined that electronic bee counters could be used to identify and monitor toxic events occurring in honey bee colonies, and also identified that 30 minutes after application, the bees did not avoid direct contact with methyl parathion. Dead bee counts, flight activity, percent return of foragers, and determination of colony composition were used to assess the effects of methyl parathion on the colony dynamics of Apis mellifera. In particular, the combination of dead bee counts, colony composition analysis, and "real time" data, provided an extensive monitoring system that enabled the progression of colony recovery to be followed, and generated information of use for the application of pesticides in the local environment. The analysis of colony composition identified that brood declined in response to decreased worker bees, and that colony recovery was dependent on brood and food reserves within the hive. The foraging activity of honey bee colonies dosed with methyl parathion was lower than that of untreated colonies because their flight activity and percent return rate declined for at least six weeks following methyl parathion application. Keywords: Honey bees, Apis mellifera, Pesticide effects, Methyl parathion, Flightmonitorin

Bio-Inspired Load Balancing In Large-Scale WSNs Using Pheromone Signalling

Wireless sensor networks (WSNs) consist of multiple, distributed nodes each with limited resources. With their strict resource constraints and application-specific characteristics, WSNs contain many challenging tradeoffs. This paper proposes a bioinspired load balancing approach, based on pheromone signalling mechanisms, to solve the tradeoff between service availability and energy consumption. We explore the performance consequences of the pheromone-based load balancing approach using (1) a system-level simulator, (2) deployment of real sensor testbeds to provide a competitive analysis of these evaluation methodologies. The effectiveness of the proposed algorithm is evaluated with different scenario parameters and the required performance evaluation techniques are investigated on case studies based on sound sensors