A Smart Sensor-Based Measurement System for Advanced Bee Hive Monitoring

Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.

Stress-induced loss of social resilience in honeybee colonies and its implications on fitness

Stressors may lead to a shift in the timing of life-history events of species, causing a mismatch with optimal environmental conditions, potentially reducing fitness. In honeybees, the timing of brood rearing and nest emergence in late winter/early spring is critical as colonies need to grow fast after winter to prepare for reproduction. However, the effects of stress on these life-history events in late winter/early spring and the possible consequences are not well understood. Therefore, we tested whether (i) honeybee colonies shift timing of brood rearing and nest emergence as response to stressors, and (ii) if there is a consequent loss of social resilience, reflected in colony fitness (survival, growth and reproduction). We monitored stressed (high load of the parasitic mite Varroa destructor or nutrition restricted) colonies and presumably non-stressed colonies from the beginning of 2020 till spring of 2021. We found that honeybee colonies do not shift the timing of brood rearing and nest emergence in spring as a coping mechanism to stressors. However, we show that there is loss of social resilience in stressed colonies, leading to reduced growth and reproduction. Our study contributes to better understanding the effects of stressors on social resilience in eusocial organisms.

Bioinspired robots can foster nature conservation

We live in a time of unprecedented scientific and human progress while being increasingly aware of its negative impacts on our planet's health. Aerial, terrestrial, and aquatic ecosystems have significantly declined putting us on course to a sixth mass extinction event. Nonetheless, the advances made in science, engineering, and technology have given us the opportunity to reverse some of our ecosystem damage and preserve them through conservation efforts around the world. However, current conservation efforts are primarily human led with assistance from conventional robotic systems which limit their scope and effectiveness, along with negatively impacting the surroundings. In this perspective, we present the field of bioinspired robotics to develop versatile agents for future conservation efforts that can operate in the natural environment while minimizing the disturbance/impact to its inhabitants and the environment's natural state. We provide an operational and environmental framework that should be considered while developing bioinspired robots for conservation. These considerations go beyond addressing the challenges of human-led conservation efforts and leverage the advancements in the field of materials, intelligence, and energy harvesting, to make bioinspired robots move and sense like animals. In doing so, it makes bioinspired robots an attractive, non-invasive, sustainable, and effective conservation tool for exploration, data collection, intervention, and maintenance tasks. Finally, we discuss the development of bioinspired robots in the context of collaboration, practicality, and applicability that would ensure their further development and widespread use to protect and preserve our natural world.

Calibration Assessment of Low-Cost Carbon Dioxide Sensors Using the Extremely Randomized Trees Algorithm

As the monitoring of carbon dioxide is an important proxy to estimate the air quality of indoor and outdoor environments, it is essential to obtain trustful data from CO₂ sensors. However, the use of widely available low-cost sensors may imply lower data quality, especially regarding accuracy. This paper proposes a new approach for enhancing the accuracy of low-cost CO₂ sensors using an extremely randomized trees algorithm. It also reports the results obtained from experimental data collected from sensors that were exposed to both indoor and outdoor environments. The indoor experimental set was composed of two metal oxide semiconductors (MOS) and two non-dispersive infrared (NDIR) sensors next to a reference sensor for carbon dioxide and independent sensors for air temperature and relative humidity. The outdoor experimental exposure analysis was performed using a third-party dataset which fit into our goals: the work consisted of fourteen stations using low-cost NDIR sensors geographically spread around reference stations. One calibration model was trained for each sensor unit separately, and, in the indoor experiment, it managed to reduce the mean absolute error (MAE) of NDIR sensors by up to 90%, reach very good linearity with MOS sensors in the indoor experiment (r² value of 0.994), and reduce the MAE by up to 98% in the outdoor dataset. We have found in the outdoor dataset analysis that the exposure time of the sensor itself may be considered by the algorithm to achieve better accuracy. We also observed that even a relatively small amount of data may provide enough information to perform a useful calibration if they contain enough data variety. We conclude that the proper use of machine learning algorithms on sensor readings can be very effective to obtain higher data quality from low-cost gas sensors either indoors or outdoors, regardless of the sensor technology.

A Monitoring System for Carbon Dioxide in Honeybee Hives: An Indicator of Colony Health

Non-dispersive infra-red (NDIR) detectors have become the dominant method for measuring atmospheric CO₂, which is thought to be an important gas for honeybee colony health. In this work we describe a microcontroller-based system used to collect data from Senserion SCD41 NDIR sensors placed in the crown boards and queen excluders of honeybee colonies. The same sensors also provide relative humidity and temperature data. Several months of data have been recorded from four different hives. The mass change measurements, from hive scales, when foragers leave the hive were compared with the data from the gas sensors. Our data suggest that it is possible to estimate the colony size from the change in measured CO₂, however no such link with the humidity is observed. Data are presented showing the CO₂ decreasing over many weeks as a colony dies.

The beehive of the future is a robot socially interacting with honeybees

A robotic beehive may unveil insights into honeybee collective behavior and sustain the colony in harsh weather.

A matter of the beehive sound: Can honey bees alert the pollution out of their hives?

Honey bees (Apis spp.) are often used as biological indicators of environmental changes. Recently, bees have been explored to monitor air contaminants by listening to the beehive sound. The beehive sound is believed to encode information on bee responses to chemicals outside their hives. Here we conducted an experiment to address this. First, we randomly fed colonies with pure syrup (PS), acetone-laced syrup (AS), or ethyl acetate-laced syrup (ES) in front of the beehives and collect the beehive sound. Based on the audio data, we build machine learning (ML) models to identify the types of syrup. The result shows that ML models achieved over 90% accuracy for identifying syrup types, indicating that the bees inside their hives emitted the sound associated with the chemicals outside their hives. Then, we sequentially fed the colonies in the order of PS, ES, and AS (the first session) and again in the reverse order (the second session), but did not remove the accumulated ES or AS in the alternative feeding experiment. Based on the audio data, the identification accuracy of both ES and AS by the ML model built on the randomly feeding experiment was different, indicating that the accumulated chemical residuals could confuse the ML models. Therefore, the beehive sound-based environmental monitoring should simultaneously consider the responses of bees to the chemicals outside their hives and their responses to those accumulated inside their hives, which may act simultaneously.

A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring

We present a custom platform that integrates data from several sensors measuring synchronously different variables of the beehive and wirelessly transmits all measurements to a cloud server. There is a rich literature on beehive monitoring. The choice of our work is not to use ready platforms such as Arduino and Raspberry Pi and to present a low cost and power solution for long term monitoring. We integrate sensors that are not limited to the typical toolbox of beehive monitoring such as gas, vibrations and bee counters. The synchronous sampling of all sensors every 5 min allows us to form a multivariable time series that serves in two ways: (a) it provides immediate alerting in case a measurement exceeds predefined boundaries that are known to characterize a healthy beehive, and (b) based on historical data predict future levels that are correlated with hive's health. Finally, we demonstrate the benefit of using additional regressors in the prediction of the variables of interest. The database, the code and a video of the vibrational activity of two months are made open to the interested readers.

Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational Resources

In precision beekeeping, the automatic recognition of colony states to assess the health status of bee colonies with dedicated hardware is an important challenge for researchers, and the use of machine learning (ML) models to predict acoustic patterns has increased attention. In this work, five classification ML algorithms were compared to find a model with the best performance and the lowest computational cost for identifying colony states by analyzing acoustic patterns. Several metrics were computed to evaluate the performance of the models, and the code execution time was measured (in the training and testing process) as a CPU usage measure. Furthermore, a simple and efficient methodology for dataset prepossessing is presented; this allows the possibility to train and test the models in very short times on limited resources hardware, such as the Raspberry Pi computer, moreover, achieving a high classification performance (above 95%) in all the ML models. The aim is to reduce power consumption and improves the battery life on a monitor system for automatic recognition of bee colony states.

Correlation of Climatic Factors with the Weight of an Apis mellifera Beehive

The bee Apis mellifera plays an important role in the balance of the ecosystem. New technologies are used for the evaluation of hives, and to determine the quality of the honey and the productivity of the hive. Climatic factors, management, flowering, and other factors affect the weight of a hive. The objective of this research was to explain the interrelationship between climatic variables and the weight of an Apis mellifera beehive using a vector autoregressive (VAR) model. The adjustment of a VAR model was carried out with seven climatic variables, and hive weight and its lags, by adjusting an equation that represents the studied hive considering all interrelationships. It was proven that the VAR (1) model can effectively capture the interrelationship among variables. The impulse response function and the variance decomposition show that the variable that most influences the hive weight, during the initial period, is the minimum dew point, which represents 5.33% of the variance. Among the variables analyzed, the one that most impacted the hive weight, after 20 days, was the maximum temperature, representing 7.50% of the variance. This study proves that it is possible to apply econometric statistical models to bee data and to relate them to climatic data, contributing significantly to the area of applied and bee statistics.

Automated Beehive Acoustics Monitoring: A Comprehensive Review of the Literature and Recommendations for Future Work

Bees play an important role in agriculture and ecology, and their pollination efficiency is essential to the economic profitability of farms. The drastic decrease in bee populations witnessed over the last decade has attracted great attention to automated remote beehive monitoring research, with beehive acoustics analysis emerging as a prominent field. In this paper, we review the existing literature on bee acoustics analysis and report on the articles published between January 2012 and December 2021. Five categories are explored in further detail, including the origin of the articles, their study goal, experimental setup, audio analysis methodology, and reproducibility. Highlights and limitations in each of these categories are presented and discussed. We conclude with a set of recommendations for future studies, with suggestions ranging from bee species characterization, to recording and testing setup descriptions, to making data and codes available to help advance this new multidisciplinary field.

IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm

A significant number of recent scientific papers have raised awareness of changes in the biological world of bees, problems with their extinction, and, as a consequence, their impact on humans and the environment. This work relies on precision beekeeping in apiculture and raises the scale of measurement and prediction results using the system we developed, which was designed to cover beehive ecosystem. It is equipped with an IoT modular base station that collects a wide range of parameters from sensors on the hive and a bee counter at the hive entrance. Data are sent to the cloud for storage, analysis, and alarm generation. A time-series forecasting model capable of estimating the volume of bee exits and entrances per hour, which simulates dependence between environmental conditions and bee activity, was devised. The applied mathematical models based on recurrent neural networks exhibited high accuracy. A web application for monitoring and prediction displays parameters, measured values, and predictive and analytical alarms in real time. The predictive component utilizes artificial intelligence by applying advanced analytical methods to find correlation between sensor data and the behavioral patterns of bees, and to raise alarms should it detect deviations. The analytical component raises an alarm when it detects measured values that lie outside of the predetermined safety limits. Comparisons of the experimental data with the model showed that our model represents the observed processes well.

Energy-Efficient Wireless Weight Sensor for Remote Beehive Monitoring

This paper proposes a new approach to the construction of an autonomous weight sensor for electronic beehive scales, constituting a crucial part in equipment used in the modern beekeeping economy. The main goal of this work is to demonstrate a methodology at the preliminary design stage leading to saving scarce energy resources necessary for the remote operation of a wireless network of beehives. The main findings of the work, achieved results, and identified threats for beekeeping scales operating in the real environment are also shown. The results presented in the article are based on actual data collected and recorded from several dozen beekeeping scales operating in the natural environment over a period of several years.

On the Importance of the Sound Emitted by Honey Bee Hives

Recent years have seen a worsening in the decline of honey bees (Apis mellifera L.) colonies. This phenomenon has sparked a great amount of attention regarding the need for intense bee hive monitoring, in order to identify possible causes, and design corresponding countermeasures. Honey bees have a key role in pollination services of both cultivated and spontaneous flora, and the increase in bee mortality could lead to an ecological and economical damage. Despite many smart monitoring systems for honey bees and bee hives, relying on different sensors and measured quantities, have been proposed over the years, the most promising ones are based on sound analysis. Sounds are used by the bees to communicate within the hive, and their analysis can reveal useful information to understand the colony health status and to detect sudden variations, just by using a simple microphone and an acquisition system. The work here presented aims to provide a review of the most interesting approaches proposed over the years for honey bees sound analysis and the type of knowledge about bees that can be extracted from sounds.

Phages in Therapy and Prophylaxis of American Foulbrood - Recent Implications From Practical Applications

American foulbrood is one of the most serious and yet unsolved problems of beekeeping around the world, because it causes a disease leading to the weakening of the vitality of honey bee populations and huge economic losses both in agriculture and horticulture. The etiological agent of this dangerous disease is an extremely pathogenic spore-forming bacterium, Paenibacillus larvae, which makes treatment very difficult. What is more, the use of antibiotics in the European Union is forbidden due to restrictions related to the prevention of the presence of antibiotic residues in honey, as well as the global problem of spreading antibiotic resistance in case of bacterial strains. The only available solution is burning of entire bee colonies, which results in large economic losses. Therefore, bacteriophages and their lytic enzymes can be a real effective alternative in the treatment and prevention of this Apis mellifera disease. In this review, we summarize phage characteristics that make them a potentially useful tool in the fight against American foulbrood. In addition, we gathered data regarding phage application that have been described so far, and attempted to show practical implications and possible limitations of their usage.

Metrology for Agriculture and Forestry 2019

This Special Issue is focused on recent advances in integrated monitoring and modelling technologies for agriculture and forestry. The selected contributions cover a wide range of topics, including wireless field sensing systems, satellite and UAV remote sensing, ICT and IoT applications for smart farming.