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

Accuracy vs. Energy: An Assessment of Bee Object Inference in Videos From On-Hive Video Loggers With YOLOv3, YOLOv4-Tiny, and YOLOv7-Tiny

A continuing trend in precision apiculture is to use computer vision methods to quantify characteristics of bee traffic in managed colonies at the hive\u27s entrance. Since traffic at the hive\u27s entrance is a contributing factor to the hive\u27s productivity and health, we assessed the potential of three open-source convolutional network models, YOLOv3, YOLOv4-tiny, and YOLOv7-tiny, to quantify omnidirectional traffic in videos from on-hive video loggers on regular, unmodified one- and two-super Langstroth hives and compared their accuracies, energy efficacies, and operational energy footprints. We trained and tested the models with a 70/30 split on a dataset of 23,173 flying bees manually labeled in 5819 images from 10 randomly selected videos and manually evaluated the trained models on 3600 images from 120 randomly selected videos from different apiaries, years, and queen races. We designed a new energy efficacy metric as a ratio of performance units per energy unit required to make a model operational in a continuous hive monitoring data pipeline. In terms of accuracy, YOLOv3 was first, YOLOv7-tiny—second, and YOLOv4-tiny—third. All models underestimated the true amount of traffic due to false negatives. YOLOv3 was the only model with no false positives, but had the lowest energy efficacy and highest operational energy footprint in a deployed hive monitoring data pipeline. YOLOv7-tiny had the highest energy efficacy and the lowest operational energy footprint in the same pipeline. Consequently, YOLOv7-tiny is a model worth considering for training on larger bee datasets if a primary objective is the discovery of non-invasive computer vision models of traffic quantification with higher energy efficacies and lower operational energy footprints

An Empirical and Theoretical Investigation of Random Reinforced Forests and Shallow Convolutional Neural Networks

For many years, the global population of honey bees has been decreasing due to inconclusive reasons resulting in the syndrome Colony Collapse Disorder (CCD). This syndrome has been plaguing bees and affecting commercial agriculture pollination since 1998. Many researchers have suggested that pesticides, in-hive chemicals, pathogens, etc., might be the causes of CCD. Researchers also believe that any changes in a beehive can disturb the bees, which may negatively affect their health. Honey bees are the most vital among all the animal pollinators contributing to approximately 30% of the world’s commercial pollination services. As they are of keystone importance to their respective ecosystems, monitoring their hives is crucial for understanding the effects of CCD and enabling beekeepers to maintain the health of their hives. As beekeepers cannot monitor their hives continuously, electronic beehive monitoring (EBM) can help them keep an eye on their hives. EBM extracts the videos, audios, temperature using cameras, microphones, sensors for observing the forager traffic (incoming and outgoing flow of the bees through the hive) to track food and nectar availability, following the sounds of the buzzing, and monitoring the abrupt temperature changes. EBM reduces the number of invasive inspections and transportation costs incurred for traveling to the beehive location. This research proposes a new technique using reinforcement learning, a method based on a reward/punishment strategy and aims at providing both accurate and energy efficient classification techniques to improve individual bee recognition in bee traffic videos

A Control And Analysis System For Honey Bee Hives

Honey bees are an important part of our everyday lives. Many foods we eat on a daily basis are dependent on some form of pollination. These foods include nuts, berries, fruits, and vegetables. In recent years, the population of honey bees has been declining due to a phenomenon called Colony Collapse Disorder. Research on this disorder is lacking and there is still much to learn. This thesis provides the details on research and development of a system that manages the data acquisition of one or more honey bee hives. This system could be used by researchers and beekeepers to monitor their beehives. The system allows users to setup parameters on various data acquisition components. The basic hive data will include audio, temperature, and humidity from inside the hive, as well as video recording the beehive entrance. The system (BeeCon) also provides basic analysis options that the hive owners can use to monitor and determine the health of their bees

Wireless sensor networks, actuation, and signal processing for apiculture

Recent United Nations reports have stressed the growing constraint of food supply for Earth's growing human population. Honey bees are a vital part of the food chain as the most important pollinator for a wide range of crops. Protecting the honey bee population worldwide, and enabling them to maximise productivity, are important concerns. This research proposes a framework for addressing these issues by considering an inter-disciplinary approach, combining recent developments in engineering and honey bee science. The primary motivation of the work outlined in this thesis was to use embedded systems technology to improve honey bee health by developing state of the art in-hive monitoring systems to classify the colony status and mechanisms to influence hive conditions. Specific objectives were identified as steps to achieve this goal: to use Wireless Sensor networks (WSN) technology to monitor a honey bee colony in the hive and collect key information; to use collected data and resulting insights to propose mechanisms to influence hive conditions; to use the collected data to inform the design of signal processing and machine learning techniques to characterise and classify the colony status; and to investigate the use of high volume data sensors in understanding specific conditions of the hive, and methods for integration of these sensors into the low-power and low-data rate WSN framework. It was found that automated, unobtrusive measurement of the in-hive conditions could provide valuable insight into the activities and conditions of honey bee colonies. A heterogeneous sensor network was deployed that monitored the conditions within hives. Data were collected periodically, showing changes in colony behaviour over time. The key parameters measured were: CO2, O2, temperature, relative humidity, and acceleration. Weather data (sunshine, rain, and temperature) were collected to provide an additional analysis dimension. Extensive energy improvements reduced the node’s current draw to 150 µA. Combined with an external solar panel, self-sustainable operation was achieved. 3,435 unique data sets were collected from five test-bed hives over 513 days during all four seasons. Temperature was identified as a vital parameter influencing the productivity and health of the colony. It was proposed to develop a method of maintaining the hive temperature in the ideal range through effective ventilation and airflow control which allow the bees involved in the activities above to engage in other tasks. An actuator was designed as part of the hive monitoring WSN to control the airflow within the hive. Using this mechanism, an effective Wireless Sensor and Actuator Network (WSAN) with Proportional Integral Derivative (PID) based temperature control was implemented. This system reached an effective set point temperature within 7 minutes of initialisation, and with steady state being reached by minute 18. There was negligible steady state error (0.0047%) and overshoot of <0.25 °C. It was proposed to develop and evaluate machine learning solutions to use the collected data to classify and describe the hive. The results of these classifications would be far more meaningful to the end user (beekeeper). Using a data set from a field deployed beehive, a biological analysis was undertaken to classify ten important hive states. This classification led to the development of a decision tree based classification algorithm which could describe the beehive using sensor network data with 95.38% accuracy. A correlation between meteorological conditions and beehive data was also observed. This led to the development of an algorithm for predicting short term rain (within 6 hours) based on the parameters within the hive (95.4% accuracy). A Random Forest based classifier was also developed using the entire collected in-hive dataset. This algorithm did not need access to data from outside the network, memory of previous measured data, and used only four inputs, while achieving an accuracy of 93.5%. Sound, weight, and visual inspection were identified as key methods of identifying the health and condition of the colony. Applications of advanced sensor methods in these areas for beekeeping were investigated. A low energy acoustic wake up sensor node for detecting the signs of an imminent swarming event was designed. Over 60 GB of sound data were collected from the test-bed hives, and analysed to provide a sound profile for development of a more advanced acoustic wake up and classification circuit. A weight measuring node was designed using a high precision (24-bit) analogue to digital converter with high sensitivity load cells to measure the weight of a hive to an accuracy of 10g over a 50 kg range. A preliminary investigation of applications for thermal and infrared imaging sensors in beekeeping was also undertaken

Automation of Feature Selection and Generation of Optimal Feature Subsets for Beehive Audio Sample Classification

The last couple of decades have witnessed an abnormal phenomenon of reduction in the bee population, this is a serious matter of concern as three out of four crops available globally have honey bee as their sole pollinator causing significant economic losses and an unbalance in the ecosystem. There have been many theories about the cause of bee colony collapses such as parasites, pesticides and poor nutrition however conclusive evidence of this phenomenon is yet to be identified. Human inspection of beehives requires precision. It takes an experienced beekeeper to determine the health of a hive by the sounds generated by the bees. If the sound indicates poor health, the beekeeper must then disrupt the hive to inspect and ascertain possible causes of poor health. This interferes with beehive activity, which can then threaten even further hive health. This work uses Feature Engineering and Machine Learning to develop techniques to monitor hive health. The thesis aims at building an automation technique for finding the best feature subsets using datasets containing different classes of audio sounds. Selecting good features forms the basis for machine learning models to further classify these audio samples. The purpose of finding the best features is to get a better audio classification which helps beekeepers know about the health of beehives and address problems such as bee immunity, effects of pesticides and environmental and nutritional stressors from remote locations

Application of Digital Particle Image Velocimetry to Insect Motion: Measurement of Incoming, Outgoing, and Lateral Honeybee Traffic

The well-being of a honeybee (Apis mellifera) colony depends on forager traffic. Consistent discrepancies in forager traffic indicate that the hive may not be healthy and require human intervention. Honeybee traffic in the vicinity of a hive can be divided into three types: incoming, outgoing, and lateral. These types constitute directional traffic, and are juxtaposed with omnidirectional traffic where bee motions are considered regardless of direction. Accurate measurement of directional honeybee traffic is fundamental to electronic beehive monitoring systems that continuously monitor honeybee colonies to detect deviations from the norm. An algorithm based on digital particle image velocimetry is proposed to measure directional traffic. The algorithm uses digital particle image velocimetry to compute motion vectors, analytically classifies them as incoming, outgoing, or lateral, and returns the classified vector counts as measurements of directional traffic levels. Dynamic time warping is used to compare the algorithm’s omnidirectional traffic curves to the curves produced by a previously proposed bee motion counting algorithm based on motion detection and deep learning and to the curves obtained from a human observer’s counts on four honeybee traffic videos (2976 video frames). The currently proposed algorithm not only approximates the human ground truth on par with the previously proposed algorithm in terms of omnidirectional bee motion counts but also provides estimates of directional bee traffic and does not require extensive training. An analysis of correlation vectors of consecutive image pairs with single bee motions indicates that correlation maps follow Gaussian distribution and the three-point Gaussian sub-pixel accuracy method appears feasible. Experimental evidence indicates it is reasonable to treat whole bees as tracers, because whole bee bodies and not parts thereof cause maximum motion. To ensure the replicability of the reported findings, these videos and frame-by-frame bee motion counts have been made public. The proposed algorithm is also used to investigate the incoming and outgoing traffic curves in a healthy hive on the same day and on different days on a dataset of 292 videos (216,956 video frames)

Acquisition, Processing, and Analysis of Video, Audio and Meteorological Data in Multi-Sensor Electronic Beehive Monitoring

In recent years, a widespread decline has been seen in honey bee population and this is widely attributed to colony collapse disorder. Hence, it is of utmost importance that a system is designed to gather relevant information. This will allow for a deeper understanding of the possible reasons behind the above phenomenon to aid in the design of suitable countermeasures. Electronic Beehive Monitoring is one such way of gathering critical information regarding a colony’s health and behavior without invasive beehive inspections. In this dissertation, we have presented an electronic beehive monitoring system called BeePi that can be placed on top of a super and requires no structural modifications to a standard beehive (Langstroth or Dadant beehive), thereby preserving the sacredness of the bee space without disturbing the natural beehive cycles. The system is capable of capturing videos of forager traffic through a camera placed over the landing pad. Audio of bee buzzing is also recorded through microphones attached outside just above the landing pad. The above sensors are connected to a low-cost raspberry pi computer, and the data is saved on the raspberry pi itself or an external hard drive. In this dissertation, we have developed an algorithm that analyzes those video recordings and returns the number of bees that have moved in each video. The algorithm is also able to distinguish between incoming, outgoing, and lateral bee movements. We believe this would help commercial and amateur beekeepers or even citizen scientists to observe the bee traffic near their respective hives to identify the state of the corresponding bee colonies. This information helps those mentioned above because it is believed that honeybee traffic carries information on colony behavior and phenology. Next, we analyzed the audio recordings and presented a system that can classify those recordings into bee buzzing, cricket chirping, and ambient noise. We later saw how a long–term analysis of the intensity of bee buzzing could help us understand the hive’s development through an entire beekeeping season. We also investigated the effect of local weather conditions using 21 different meteorological variables on the forager traffic. We collected the meteorological data from a weather station located on the campus of Utah State University. Through our study, we were able to show that without the use of additional costly intrusive hardware to count the bees, we can use our bee motion counting algorithm to calculate the bee motions and then use the counts to investigate the relationship between foraging activity and local weather. To ensure that our findings and algorithms can be reproduced, we have made our datasets and source codes public for interested research and citizen science communities