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Journal articles on the topic 'Hive Monitoring'
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1
Khrystyuk, Andriy, and Kirpichnikov Anton. "Automatic apiary care system." Modeling Control and Information Technologies, no. 5 (November 21, 2021): 54–55. http://dx.doi.org/10.31713/mcit.2021.16.
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An automatic control system for remote control and monitoring of bee life parameters in hives is developed. Based on the research of the main parameters of the life of bees in the hive, we have developed a scheme for managing and monitoring the weight of the hive, temperature and humidity, noise level, prevention of theft.
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Kuch, Ostap, and Ilona Lahun. "APIARY MONITORING AND AUTOMATION IOT SYSTEM." Measuring Equipment and Metrology 83, no. 4 (2022): 24–29. http://dx.doi.org/10.23939/istcmtm2022.04.024.
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A system of remote monitoring and automation apiary has been developed. This is a low-cost and scalable solution designed for deployment in distant rural areas. An unconventional solution is applying the industrial standard Modbus protocol to transfer data from the hives to a central server. This made it possible to reduce the cost of the system and standardize it. Monitoring the temperature and humidity inside the hives is important for analyzing the condition of bee colonies. The automation of the temperature and humidity control process is implemented based on a fuzzy model of the servo drive of the hive door.
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Bricout, Augustin, Philippe Leleux, Pascal Acco, Christophe Escriba, Jean-Yves Fourniols, Georges Soto-Romero, and Rémi Floquet. "Bee Together: Joining Bee Audio Datasets for Hive Extrapolation in AI-Based Monitoring." Sensors 24, no. 18 (September 19, 2024): 6067. http://dx.doi.org/10.3390/s24186067.
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Beehive health monitoring has gained interest in the study of bees in biology, ecology, and agriculture. As audio sensors are less intrusive, a number of audio datasets (mainly labeled with the presence of a queen in the hive) have appeared in the literature, and interest in their classification has been raised. All studies have exhibited good accuracy, and a few have questioned and revealed that classification cannot be generalized to unseen hives. To increase the number of known hives, a review of open datasets is described, and a merger in the form of the “BeeTogether” dataset on the open Kaggle platform is proposed. This common framework standardizes the data format and features while providing data augmentation techniques and a methodology for measuring hives’ extrapolation properties. A classical classifier is proposed to benchmark the whole dataset, achieving the same good accuracy and poor hive generalization as those found in the literature. Insight into the role of the frequency of the classification of the presence of a queen is provided, and it is shown that this frequency mostly depends on a colony’s belonging. New classifiers inspired by contrastive learning are introduced to circumvent the effect of colony belonging and obtain both good accuracy and hive extrapolation abilities when learning changes in labels. A process for obtaining absolute labels was prototyped on an unsupervised dataset. Solving hive extrapolation with a common open platform and contrastive approach can result in effective applications in agriculture.
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König, Andreas. "An in-hive soft sensor based on phase space features for <i>Varroa</i> infestation level estimation and treatment need detection." Journal of Sensors and Sensor Systems 11, no. 1 (January 20, 2022): 29–40. http://dx.doi.org/10.5194/jsss-11-29-2022.
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Abstract. Bees are recognized as an indispensable link in the human food chain and general ecological system. Numerous threats, from pesticides to parasites, endanger bees, enlarge the burden on hive keepers, and frequently lead to hive collapse. The Varroa destructor mite is a key threat to bee keeping, and the monitoring of hive infestation levels is of major concern for effective treatment. Continuous and unobtrusive monitoring of hive infestation levels along with other vital bee hive parameters is coveted, although there is currently no explicit sensor for this task. This problem is strikingly similar to issues such as condition monitoring or Industry 4.0 tasks, and sensors and machine learning bear the promise of viable solutions (e.g., creating a soft sensor for the task). In the context of our IndusBee4.0 project, following a bottom-up approach, a modular in-hive gas sensing system, denoted as BeE-Nose, based on common metal-oxide gas sensors (in particular, the Sensirion SGP30 and the Bosch Sensortec BME680) was deployed for a substantial part of the 2020 bee season in a single colony for a single measurement campaign. The ground truth of the Varroa population size was determined by repeated conventional method application. This paper is focused on application-specific invariant feature computation for daily hive activity characterization. The results of both gas sensors for Varroa infestation level estimation (VILE) and automated treatment need detection (ATND), as a thresholded or two-class interpretation of VILE, in the order of up to 95 % are presented. Future work strives to employ a richer sensor palette and evaluation approaches for several hives over a bee season.
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Nour Elhouda Bakri, Khaled Bouchoucha, Walid Nagara, and M’Naouer Djemali. "Enhancing honeybee breeding for sustainable agriculture through temperature and relative humidity monitoring." World Journal of Advanced Research and Reviews 21, no. 2 (February 28, 2024): 286–92. http://dx.doi.org/10.30574/wjarr.2024.21.2.0412.
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Through their vital role in pollination, honeybee colonies play a crucial part in sustaining biodiversity and ensuring global food security. This paper aims to: 1) assess temperature and relative humidity variations within the hive and at the brood level; 2) determine genetic parameters for these traits; and 3) predict Breeding Values (PBVs) for honeybee colonies. Temperature and relative humidity data, during the period 2020-2023, were collected in Northern Tunisia using sensors placed inside hives and at the brood level. A dataset comprising 214,128 records for temperature and relative humidity within hives, sourced from 317 devices, was used in this study. Additionally, 20,740 records for temperature and relative humidity obtained from 78 brood-level devices were incorporated into the analysis. Phenotypic and genetic parameters were computed for the four examined traits, and using a BLUP Animal model, colony breeding values (PBVs) were predicted. Main results indicated a highly significant influence (p<0.01) of the month effect on the four temperature and relative humidity traits. Heritability estimates for in-hive temperature, in-hive relative humidity, brood relative humidity, and brood temperature were 0.14, 0.12, 0.16, and 0.28, respectively. Positive correlations were observed between relative humidity inside hives and at the brood level, as well as between temperature within beehives and at the brood level. Colony breeding values were predicted to select the best adapted bee queens to enhance honeybee’s sustainable use under Southern Mediterranean climatic conditions.
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Krishnasamy, Venkateswaran, N. Sridhar, and L. Niranjan. "IoT-Based Beehive Monitoring System for Real-Time Monitoring of Apis cerana indica Colonies." Sociobiology 70, no. 4 (October 20, 2023): e9352. http://dx.doi.org/10.13102/sociobiology.v70i4.9352.
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A study was conducted to monitor the bee activity in the colonies of diferente strengths in real time using an IoT-based device. The in-hive temperature and relative humidity were measured in the colonies of Apis cerana indica Fabricius of different strengths using the sensor-laden IoT device that was correlated with the movement of foragers into and out of the hive. A significantly higher movement of foragers was recorded at an in-hive temperature and relative humidity of 27.84 ºC and 61.47% at 5-6 p.m. with an observed activity of 9,638 bees/hive/hour in the strong colonies. In the weak colonies, the mean forager activity was 1,436.3 bees/hive/hour, which was recorded at an in-hive temperature of 26.52 ºC and 61.42% relative humidity. The mean honey area in the strong and weak colonies were 1,300.80±177.61 cm2 and 508.80±156.84 cm2, respectively. Pollen area in the strong and weak colonies were 447.60±112.08 cm2 and 116.20±66.43 cm2, respectively. In the strong and weak colonies, the area under egg brood was 470±53.06 cm2 and 88.20±36.85 cm2, larvae brood was 583.40±11.04 cm2 and 80.00±24.67 cm2 and sealed brood was 684.20±57.98 cm2 and 102.80±16.59 cm2, respectively. The real-time data on the movement of foragers in the colonies of different strengths enabled us to undertake timely intervention in the maintenance of the bee colonies.
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Grigoryan, Leonty Rustemovich, Maxim Sergeevich Kovalenko, Anastasia Leontyevna Grigoryan, and Dmitry Yuryevich Paroshin. "Intellectual beehives monitoring system." Agrarian Scientific Journal, no. 10 (October 23, 2019): 59–65. http://dx.doi.org/10.28983/asj.y2019i10pp59-65.
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A system for remote monitoring of the parameters of bee hives is considered. The problem of remote monitoring at long distances, taking into account the changing weather factors, is very relevant. Among the variety of hardware for keeping bees, there is currently no unified monitoring system. The studies carried out allowed us to determine the optimal circuitry and software components of the construction of a monitoring system. The result of the development of a system with a local hive monitoring system and a remote data collection system using cloud technologies is presented in this work.
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Catania, Pietro, and Mariangela Vallone. "Application of A Precision Apiculture System to Monitor Honey Daily Production." Sensors 20, no. 7 (April 3, 2020): 2012. http://dx.doi.org/10.3390/s20072012.
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Precision beekeeping or precision apiculture is an apiary management strategy based on the monitoring of individual bee colonies to minimize resource consumption and maximize the productivity of bees. Bees play a fundamental role in ensuring pollination; they can also be considered as indicators of the state of pollution and are used as bio monitors. Beekeeping needs continuous monitoring of the animals and can benefit from advanced intelligent ambiance technologies. The aim of this study was the design of a precision apiculture system (PAS) platform for monitoring and controlling the following environmental parameters: wind, temperature, and relative humidity inside and outside the hive, in order to assess their influence on honey production. PAS is based on an Arduino board with an Atmel microcontroller, and the connection of a load cell for recording the weight of the hive, relative humidity and temperature sensor inside the hive, and relative humidity and temperature sensor outside the hive using an anemometer. PAS was installed in common hives and placed in an open field in a French honeysuckle plot; the system was developed to operate in continuous mode, monitoring the period of 24 April–1 June 2019. Temperature was constant in the monitored period, around 35 °C, inside the hive, proving that no criticalities occurred regarding swarming or absconding. In the period between 24 and 28 May, a lack of honey production was recorded, attributed to a lowering of the external temperature. PAS was useful to point out the eventual reduction in honey production due to wind; several peaks of windiness exceeding 5 m s−1 were recorded, noting that honey production decreases with the peaks in wind. Therefore, the data recorded by PAS platform provided a valid decisional support to the operator. It can be implemented by inserting additional sensors for detecting other parameters, such as rain or sound.
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Newton, Michael I., Luke Chamberlain, Adam McVeigh, and Martin Bencsik. "Winter Carbon Dioxide Measurement in Honeybee Hives." Applied Sciences 14, no. 4 (February 19, 2024): 1679. http://dx.doi.org/10.3390/app14041679.
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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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Sanz, Milagros Casado, Rubén Prado-Jimeno, and Juan Francisco Fuentes-Pérez. "Comparative Study of Natural Fibres to Improve Insulation in Wooden Beehives Using Sensor Networks." Applied Sciences 14, no. 13 (July 1, 2024): 5760. http://dx.doi.org/10.3390/app14135760.
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The beekeeping sector is increasingly focused on creating optimal and natural environments for honeybees to reduce dependence on external factors, especially given progressively hotter summers. Improving hive thermal conditions can enhance bee wellbeing and production. While pinewood hives are predominant, some have started using insulating materials like polystyrene. However, many synthetic materials, despite their excellent insulation properties, are incompatible with organic food production, requiring alternative solutions. This study compares the thermal insulation properties of various natural materials, including white and black agglomerated cork, wood fibres, and rock mineral wool. These materials are potentially compatible with organic food production. Additionally, the research evaluates cost-effective sensor networks to monitor bioclimatic variables in real time. Lab tests using a Langstroth-type hive with a controlled heat source were conducted, monitoring temperature and humidity inside and outside the hive. The results revealed that all selected materials provided similar thermal insulation, superior to a hive without insulation. This finding suggests that using natural materials can enhance hive thermal comfort (i.e., the material’s ability to maintain a stable internal temperature), thereby improving honeybee wellbeing and productivity in a manner compatible with organic food production.
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Dragomir, Florin, Otilia Elena Dragomir, and Adrian Oprea. "Stand-Alone Power System for Monitoring and Control of the Temperature." Applied Mechanics and Materials 291-294 (February 2013): 2570–73. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.2570.
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The main objective of the article is to stimulate interest of the consumers for the green energy considering the environmental and the financial impact (in this case by increasing productivity) of this one. For these arguments, this article proposes a real application, with autonomy in the power supply, for control the temperature in the hive using energy from alternative sources (through a solar panel). The temperature regulation is controlled by a microcontroller. For setting the temperature of the hive some bees are used at the expense of honey production. If there would be no need to adjust the temperature of the hive bees when the hive would increase productivity (worker bees population and honey production).
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Bencsik, Martin, Adam McVeigh, Costas Tsakonas, Tarun Kumar, Luke Chamberlain, and Michael I. Newton. "A Monitoring System for Carbon Dioxide in Honeybee Hives: An Indicator of Colony Health." Sensors 23, no. 7 (March 29, 2023): 3588. http://dx.doi.org/10.3390/s23073588.
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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.
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Daisley, Brendan A., Andrew P. Pitek, John A. Chmiel, Kait F. Al, Anna M. Chernyshova, Kyrillos M. Faragalla, Jeremy P. Burton, Graham J. Thompson, and Gregor Reid. "Novel probiotic approach to counter Paenibacillus larvae infection in honey bees." ISME Journal 14, no. 2 (October 29, 2019): 476–91. http://dx.doi.org/10.1038/s41396-019-0541-6.
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Abstract American foulbrood (AFB) is a highly virulent disease afflicting honey bees (Apis mellifera). The causative organism, Paenibacillus larvae, attacks honey bee brood and renders entire hives dysfunctional during active disease states, but more commonly resides in hives asymptomatically as inactive spores that elude even vigilant beekeepers. The mechanism of this pathogenic transition is not fully understood, and no cure exists for AFB. Here, we evaluated how hive supplementation with probiotic lactobacilli (delivered through a nutrient patty; BioPatty) affected colony resistance towards a naturally occurring AFB outbreak. Results demonstrated a significantly lower pathogen load and proteolytic activity of honey bee larvae from BioPatty-treated hives. Interestingly, a distinctive shift in the microbiota composition of adult nurse bees occurred irrespective of treatment group during the monitoring period, but only vehicle-supplemented nurse bees exhibited higher P. larvae loads. In vitro experiments utilizing laboratory-reared honey bee larvae showed Lactobacillus plantarum Lp39, Lactobacillus rhamnosus GR-1, and Lactobacillus kunkeei BR-1 (contained in the BioPatty) could reduce pathogen load, upregulate expression of key immune genes, and improve survival during P. larvae infection. These findings suggest the usage of a lactobacilli-containing hive supplement, which is practical and affordable for beekeepers, may be effective for reducing enzootic pathogen-related hive losses.
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Ai, Hiroyuki, and Shinya Takahashi. "The Lifelog Monitoring System for Honeybees: RFID and Camera Recordings in an Observation Hive." Journal of Robotics and Mechatronics 33, no. 3 (June 20, 2021): 457–65. http://dx.doi.org/10.20965/jrm.2021.p0457.
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A typical honeybee colony contains more than 15,000 individuals, each with its own task related to supporting the hive and maintaining the colony. In previous studies on honeybees, observing individual animals’ behaviors has been a difficult and time-consuming task to understand the relationship between in-hive communication and environmental changes outside the hive, therefore it is necessary in any attempt to develop applying a remote sensing technology. To allow researchers to pass much of this tracking work on to computers, we have developed the lifelog monitoring system for honeybees, which uses RFID and Raspberry Pi camera recordings. Our preliminary experiments consisted of several tests aimed at identifying the optimal conditions for this system. First, two commercial RFID readers with antennas were compared in terms of their sensitivity to signals from RFID tags placed at various distances. We found that the UP16-1000-J2 reader was much more sensitive and had a longer effective range compared to the UP4-200-J2. The most sensitive region in the RFID antenna on the UP16-1000-J2 reader was 30 mm long and 5 mm wide at its center. Based on this preliminary information, we designed and built a passage from the interior of the observation hive to the outside so that all RFID-tagged bees could be detected individually by the RFID reader as they walked through the passage. Moreover, to detect the direction of either departure or arrival of each bee, we placed two RFID antennas under the passage between the observation hive and the outside, one near each end of the passage. All departure and arrival times of RFID-tagged bees were detected with their ID numbers. Using recorded data from these two RFID readers, we could measure how much time each tagged bee spent outside the hive. In addition to RFID recording on the passage, we also tracked all in-hive movements of numbered RFID-tagged honeybees. In-hive movements were simultaneously, comprehensively and automatically recorded via six Raspberry Pi camera modules arranged on the two sides of the observation hive. The cameras were set to record from 6:30 to 19:30 every day for one month, once or twice each year from 2015 to 2018. The in-hive behaviors of these bees were analyzed according to a simultaneous tracking algorithm that we developed for this purpose. Data from the monitoring system revealed that time spent outside the hive increased markedly after following the waggle dance. In addition to its findings on bee behavior, this study also confirms the effectiveness of our recording system combining RFID and Raspberry Pi cameras for honeybee lifelog monitoring.
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ANDRIJEVIĆ, Nebojša, Dejana HERCEG, Srđan MARIČIĆ, Vladan RADIVOJEVIĆ, and Goran JOCIĆ. "CONCEPT SOLUTION OF AUTONOMOUS IOT SMART HIVE AND OPTIMIZATION OF ENERGY CONSUMPTION USING ARTIFICIAL INTELLIGENCE." Journal of process management and new technologies 12, no. 1-2 (May 19, 2024): 41–48. http://dx.doi.org/10.5937/jpmnt12-49567.
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In this paper the authors present a conceptual solution for an autonomous smart beehive with a focus on energy efficiency; the hive's existence is based on artificial intelligence. The hive is equipped with an advanced system for monitoring the entry and exit of bees, as well as for collecting data on the weather inside and around the hive. Using an array of sensors controlled by Espressif ESP32 and Arduino Mega microcontroller boards, the hive continuously optimizes the operation of the ventilation system and other components, monitoring energy consumption and adapting to changing conditions. Special accents in the work are dedicated to the monitoring of the solar panel and, consequently, the capacity of the battery for independent power supply of the system, as well as the application of artificial intelligence to predict meteorological changes and optimize energy efficiency. This paper provides a comprehensive overview of the solutions and technologies that enable the autonomous and energy-efficient functioning of the Smart Hive.
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Ziegler, Cristiano, Renan Mitsuo Ueda, Tiago Sinigaglia, Felipe Kreimeier, and Adriano Mendonça Souza. "Correlation of Climatic Factors with the Weight of an Apis mellifera Beehive." Sustainability 14, no. 9 (April 28, 2022): 5302. http://dx.doi.org/10.3390/su14095302.
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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.
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Muhammad Ammar Asyraf Che Ali, Bukhari Ilias, Norasmadi Abdul Rahim, Shazmin Aniza Abdul Shukor, Abdul Hamid Adom, Mohd Al-haffiz Saad, and Mohd Fauzi Abu Hassan. "Development of Artificial Stingless Bee Hive Monitoring using IoT System on Developing Colony." Journal of Advanced Research in Applied Sciences and Engineering Technology 33, no. 2 (November 1, 2023): 254–68. http://dx.doi.org/10.37934/araset.33.2.254268.
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The trend of stingless bees’ farm in Malaysia has increased recently as it has been proven that its honey gives various benefits to human beings. This trend requires beekeepers to do more frequent inspections of beehives. However, the current practice of opening the cover to inspect the colony and honey will disrupt colony activity. According to a recent study, these stingless bees can only survive between 22 and 38 degrees Celsius, and harsh weather conditions might lead to the collapse of bee colonies. In order to ensure a consistent honey production, the IoT monitoring system will be implemented on an artificial stingless beehive. The system is equipped with an embedded system that utilizes a NodeMCU ESP8266, temperature and humidity sensors, and load cell sensors. Next, honey compartment weight, temperature and humidity inside stingless beehive, and temperature and humidity outside stingless beehive will be uploaded to the Internet of Things (IoT) platforms, namely Thingspeak and Cayenne. The data is sent to Thingspeak via the REST API while to Cayenne by the MQTT API. All data from the artificial stingless bee hive indicating the occurrence of colony rising and has been uploaded to the IoT platform. By analysing the data that were recorded for 13 days, all of the input data such as the weight of the honey compartment, the temperature in the hive, and the humidity in the hive, display its respective characteristics. For the honey compart weight, it has been found that the stingless bee colony is rising as a result of the increasing honey and colony in the compartment weight. Regarding the hive temperature, it has been determined that the temperature inside the hive is stable around 26°C to 38°C in normal weather conditions. Whereas for humidity inside the hive, it is remained between 76.5% and 85.6% due to the moisture from the honey inside the compartment. Lastly, these results indicate that the colony living in the artificial hive of stingless bees is healthy and growing.
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Evsyukova, V. K., M. S. Savvinova, V. V. Sysolyatina, F. V. Nikolaeva, and A. Ya Fedorov. "Innovative method for optimizing the microclimate of the winter hive for bees in the conditions of the cryolithozone." E3S Web of Conferences 282 (2021): 07022. http://dx.doi.org/10.1051/e3sconf/202128207022.
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The article discusses the practical significance of the use of infrared scanning method for optimizing the microclimate of winter hives and monitoring bee colonies in extreme conditions of cryolithozone. When monitoring the microclimate by generally accepted zoohygienic methods, the devices record specific indicators of a particular parameter (temperature, humidity, air velocity, pressure, noise, light), while the reason for the deviation from the standard indicators for microclimate optimization is not always possible to find out. When using the infrared scanning method with the Irtis 2000SN thermal imager, it was possible to determine the reasons for the deviation of the parameters of the winter hive microclimate. The targeted elimination of defects in the enclosing structures detected by infrared scanning made it possible to quickly optimize the parameters of the winter hive microclimate. Contactless monitoring of the state of bee colonies during the winter dormancy. This early informative diagnosis without stress allows to identify a problem bee colony and take timely rescue measures. The analysis of the wintering results showed that the proportion of successful wintering in 2019 was 90%, which is 20% more than in 2018 and 30% more than in 2017.
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Rafael Braga, Antonio, Juliana De Castro Rabelo, Arthur De Castro Callado, Atslands Rego da Rocha, Breno M. Freitas, and Danielo G. Gomes. "BeeNotified! A Notification System of Physical Quantities for Beehives Remote Monitoring." Revista de Informática Teórica e Aplicada 27, no. 3 (June 18, 2020): 50–61. http://dx.doi.org/10.22456/2175-2745.90724.
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One of the ways to reduce inappropriate management of hives and monitor bee health is to send notifications/alerts about the data collected through sensors. This study presents BeeNotified!, a solution for sending notifications through Telegram, e-mail, and SMS. The notifications warn about the level of temperature, humidity, sound, carbon dioxide, oxygen, hive weight and delay in data gathering. From this data, researchers and beekeepers can be informed and make changes in the locations of the hives, avoiding catastrophes and possible diseases. The results obtained with the processing time in the sending of messages showed that the messages sent via SMS and Telegram have a shorter processing time compared to the sending via e-mail. In regards to sending notifications according to user preferences, all notifications were sent correctly.
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Terenzi, Alessandro, Stefania Cecchi, and Susanna Spinsante. "On the Importance of the Sound Emitted by Honey Bee Hives." Veterinary Sciences 7, no. 4 (October 31, 2020): 168. http://dx.doi.org/10.3390/vetsci7040168.
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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.
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Sledevič, Tomyslav, Artūras Serackis, and Darius Plonis. "FPGA Implementation of a Convolutional Neural Network and Its Application for Pollen Detection upon Entrance to the Beehive." Agriculture 12, no. 11 (November 4, 2022): 1849. http://dx.doi.org/10.3390/agriculture12111849.
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The condition of a bee colony can be predicted by monitoring bees upon hive entrance. The presence of pollen grains gives beekeepers significant information about the well-being of the bee colony in a non-invasive way. This paper presents a field-programmable-gate-array (FPGA)-based pollen detector from images obtained at the hive entrance. The image dataset was acquired at native entrance ramps from six different hives. To evaluate and demonstrate the performance of the system, various densities of convolutional neural networks (CNNs) were trained and tested to find those suitable for pollen grain detection at the chosen image resolution. We propose a new CNN accelerator architecture that places a pre-trained CNN on an SoC FPGA. The CNN accelerator was implemented on a cost-optimized Z-7020 FPGA with 16-bit fixed-point operations. The kernel binarization and merging with the batch normalization layer were applied to reduce the number of DSPs in the multi-channel convolutional core. The estimated average performance was 32 GOPS for a single convolutional core. We found that the CNN with four convolutional and two dense layers gave a 92% classification accuracy, and it matched those declared for state-of-the-art methods. It took 8.8 ms to classify a 512 × 128 px frame and 2.4 ms for a 256 × 64 px frame. The frame rate of the proposed method outperformed the speed of known pollen detectors. The developed pollen detector is cost effective and can be used as a real-time image classification module for hive status monitoring.
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Braga, Diogo, Ana Madureira, Fabio Scotti, Vincenzo Piuri, and Ajith Abraham. "An Intelligent Monitoring System for Assessing Bee Hive Health." IEEE Access 9 (2021): 89009–19. http://dx.doi.org/10.1109/access.2021.3089538.
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Takahashi, Shinya, Koji Hashimoto, Sakashi Maeda, Naoyuki Tsuruta, and Hiroyuki Ai. "Development of Behavior Monitoring System for Honeybees in Hive." Transactions of the Japanese Society for Artificial Intelligence 32, no. 4 (2017): B—GC2_1–11. http://dx.doi.org/10.1527/tjsai.b-gc2.
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Kulyukin, Vladimir A., and Aleksey V. Kulyukin. "Accuracy vs. Energy: An Assessment of Bee Object Inference in Videos from On-Hive Video Loggers with YOLOv3, YOLOv4-Tiny, and YOLOv7-Tiny." Sensors 23, no. 15 (July 29, 2023): 6791. http://dx.doi.org/10.3390/s23156791.
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A continuing trend in precision apiculture is to use computer vision methods to quantify characteristics of bee traffic in managed colonies at the hive’s entrance. Since traffic at the hive’s entrance is a contributing factor to the hive’s 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.
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Bowles, Tim, Kevin M. Trentino, Adam Lloyd, Laura Trentino, Kevin Murray, Aleesha Thompson, Frank M. Sanfilippo, and Grant Waterer. "Health in a Virtual Environment (HIVE): A Novel Continuous Remote Monitoring Service for Inpatient Management." Healthcare 12, no. 13 (June 26, 2024): 1265. http://dx.doi.org/10.3390/healthcare12131265.
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The aim of this study was to describe the implementation of a novel 50-bed continuous remote monitoring service for high-risk acute inpatients treated in non-critical wards, known as Health in a Virtual Environment (HIVE). We report the initial results, presenting the number and type of patients connected to the service, and assess key outcomes from this cohort. This was a prospective, observational study of characteristics and outcomes of patients connected to the HIVE continuous monitoring service at a major tertiary hospital and a smaller public hospital in Western Australia between January 2021 and June 2023. In the first two and a half years following implementation, 7541 patients were connected to HIVE for a total of 331,118 h. Overall, these patients had a median length of stay of 5 days (IQR 2, 10), 11.0% (n = 833) had an intensive care unit admission, 22.4% (n = 1691) had an all-cause emergency readmission within 28 days from hospital discharge, and 2.2% (n = 167) died in hospital. Conclusions: Our initial results show promise, demonstrating that this innovative approach to inpatient care can be successfully implemented to monitor high-risk patients in medical and surgical wards. Future studies will investigate the effectiveness of the program by comparing patients receiving HIVE supported care to comparable patients receiving routine care.
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O.O., Zhukov, and Horbenko V.I. "АДАПТИВНИЙ ПІДХІД ДО ВИЗНАЧЕННЯ СТАНУ ВУЛИКА ЗА ДОПОМОГОЮ НЕЙРОННИХ МЕРЕЖ ТА АНАЛІЗУ АУДІО." System technologies 4, no. 153 (May 1, 2024): 3–12. http://dx.doi.org/10.34185/1562-9945-4-153-2024-01.
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Problem statement. Monitoring the queen bee is crucial for the health and produc-tivity of a bee colony. The queen plays a vital role in reproduction and maintaining the colony's population. Utilizing neural networks, such as CNNs along with sound analysis, can be a valuable tool for monitoring queen bees and assessing their behavior and health within the hive. Purpose. Finding the best way to preprocess audio data and review it is an important task that, if performed well, will help to track the bee hive population and its health state in general. Related work. With the development of deep learning, several studies were done on their application along with sound analysis in bee hive state identi-fication, such as swarming or bee queen detection. Materials and methods. A public la-beled dataset “To bee or not to bee” with bee hive sounds was used for training. It was split by source and then cut into pieces 4 seconds each. Then, CNN models were trained using different audio feature extraction methods, such as MFCCs and STFT. First, it was trained on a first audio set, and then trained models were utilized to analyze their per-formance on the evaluation set of the second audio set. Results and discussion. According to the training and evaluation results, MFCCs-based models have given constant good results, and when used on a limited audio dataset, pre-trained model showed better per-formance than the one trained from zero. Conclusions. The experiment showed that MFCCs is a better performant feature extraction method for a task of the bee hive sounds analysis and bee queen presence identification. Also, even though training a model on a full audio set results in better performance, pre-trained models can detect a bee queen absence in another hive even after an additional training on a limited audio dataset.
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Atanasov, Atanas Z., Ivaylo S. Hristakov, Kaloyan E. Stoyanov, and Presiyan А. Zhelyazkov. "Monitoring honeybee (Apis mellifera) flight activity and within-day hive weight changes during rapeseed (Brassica napus) flowering." Technium: Romanian Journal of Applied Sciences and Technology 14 (October 9, 2023): 90–93. http://dx.doi.org/10.47577/technium.v14i.9685.
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Honeybee (Apis mellifera) flight activity and beehive changing weight during rapeseed (Brassica napus) flowering was evaluated. The research was carried out during the 2021 agricultural period in the northeastern region of Bulgaria, specifically in Yuper village. The experimental site is situated at 43°54'28.59" N, 26°23'49.02"E coordinates, with an elevation of 107 meters. Within the experiment, there were 130 bee colonies kept in Dadant-Blatt hives. The bees belong to the species known as (Apis mellifera macedonica). Throughout the research duration, the study focuses on tracking the quantity of bees leaving the hive (Nbee) and the changes in hive weight (Y) caused by the secretion of nectar during different time intervals throughout the day. The monitoring included external factors such as the temperature of the outside air (Tair), 0C, the humidity of the air (Hair) %,, and the surface temperature (Tsur) 0C, of the rapeseed inflorescences. The findings indicate a noteworthy and favorable relationship between Nbee and Y. The stronger correlation between Nbee, Y and Tsur was found. The other weather indicators as Tair and Hair are not correlated with Nbee and Y. The monitoring of the honeybee flight activity and beehive weight during rapeseed flowering will help the beekeepers to determine the suitable moment to expand the bee colonies in order to achieve optimal productivity.
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Drummond, Francis A., and Aaron Kinyu Hoshide. "An Economic Cost/Benefit Tool to Assess Bee Pollinator Conservation, Pollination Strategies, and Sustainable Policies: A Lowbush Blueberry Case Study." Sustainability 16, no. 8 (April 12, 2024): 3242. http://dx.doi.org/10.3390/su16083242.
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Lowbush blueberry is a mass-flowering plant species complex that grows in both unmanaged wild landscapes and managed agricultural fields in northeastern regions of both the USA and Canada. During pollination, more than 120 native bee species are associated with lowbush blueberry ecosystems in Maine, USA, in addition to three commercially managed bees. Over a 29-year period, we sampled 209 lowbush blueberry fields using quadrat and transect sampling, recording both native bee and honey bee densities, honey bee hive stocking density, and native bees as a proportion of total bees. These data were used to simulate economic uncertainty in pollination. We developed a novel algorithm, the Economic Pollinator Level (EPL), to estimate bee densities that economically warrant pollination investments such as rented hives and planting bee pastures. Statistical modeling indicated both native bee and honey bee activity density predicted proportion fruit set in fields. Honey bee activity density was well predicted by hive stocking density. Proportion fruit set adequately predicted yield. EPL was most sensitive to fruit set/m2/bee and less dependent on berry weight, rented hive stocking density, hive rental cost, lowbush blueberry price, and the annual cost of planting/maintaining pollinator pastures. EPL can be used to sustainably balance economical pollination investments/decisions with bee conservation in lowbush blueberry crops and potentially other pollinator-dependent crops.
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Kulyukin, Vladimir A., Daniel Coster, Aleksey V. Kulyukin, William Meikle, and Milagra Weiss. "Discrete Time Series Forecasting of Hive Weight, In-Hive Temperature, and Hive Entrance Traffic in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part I." Sensors 24, no. 19 (October 4, 2024): 6433. http://dx.doi.org/10.3390/s24196433.
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From June to October, 2022, we recorded the weight, the internal temperature, and the hive entrance video traffic of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, AZ, USA. The weight and temperature were recorded every five minutes around the clock. The 30 s videos were recorded every five minutes daily from 7:00 to 20:55. We curated the collected data into a dataset of 758,703 records (280,760–weight; 322,570–temperature; 155,373–video). A principal objective of Part I of our investigation was to use the curated dataset to investigate the discrete univariate time series forecasting of hive weight, in-hive temperature, and hive entrance traffic with shallow artificial, convolutional, and long short-term memory networks and to compare their predictive performance with traditional autoregressive integrated moving average models. We trained and tested all models with a 70/30 train/test split. We varied the intake and the predicted horizon of each model from 6 to 24 hourly means. Each artificial, convolutional, and long short-term memory network was trained for 500 epochs. We evaluated 24,840 trained models on the test data with the mean squared error. The autoregressive integrated moving average models performed on par with their machine learning counterparts, and all model types were able to predict falling, rising, and unchanging trends over all predicted horizons. We made the curated dataset public for replication.
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Hunor, Bartos, Zsolt Bodor, Ágnes Keresztesi, George Gârbacea, Deak György, Matei Monica, Lucian Laslo, Madalina Boboc, H. Elena, and Róbert Szép. "Advances in Beehive Monitoring Systems: Low-Cost Integrating Sensor Technology for Improved Apiculture Management." E3S Web of Conferences 589 (2024): 04001. http://dx.doi.org/10.1051/e3sconf/202458904001.
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The integration of monitoring systems in beekeeping has significant implications for the health and sustainability of honeybee colonies. These advanced systems, which include sensors for temperature, humidity, hive weight, and sound analysis, allow for real-time tracking of hive conditions, enabling beekeepers to respond promptly to potential threats such as disease, pests, or environmental stressors. Research shows that such technology can lead to improved colony management, reducing the incidence of colony collapse disorder (CCD) by facilitating early intervention. Additionally, continuous data collection helps in identifying patterns and anomalies in hive behavior, contributing to a better understanding of bee biology and environmental interactions. However, the effectiveness of these systems depends on the accuracy of the data collected and the beekeeper’s ability to interpret and act upon this information. Moreover, while monitoring systems have the potential to enhance colony health, there are concerns about the cost, accessibility, and the need for technical expertise, which could limit widespread adoption among smallscale beekeepers. Overall, the use of monitoring systems in beehives represents a promising tool for enhancing bee colony health, though its success will rely on overcoming the challenges of implementation and ensuring that beekeepers can utilize the data effectively to support their colonies.
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Thompson, Aleesha, Drianca Naidoo, Eliza Becker, Kevin M. Trentino, Dharjinder Rooprai, and Kenneth Lee. "Remote Monitoring and Virtual Appointments for the Assessment and Management of Depression via the Co-HIVE Model of Care: A Qualitative Descriptive Study of Patient Experiences." Healthcare 12, no. 20 (October 18, 2024): 2084. http://dx.doi.org/10.3390/healthcare12202084.
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Objective: This qualitative study sought to explore patient experiences with technologies used in the Community Health in a Virtual Environment (Co-HIVE) pilot trial. Technology is becoming increasingly prevalent in mental healthcare, and user acceptance is critical for successful adoption and therefore clinical impact. The Co-HIVE pilot trialled a model of care whereby community-dwelling patients with symptoms of depression utilised virtual appointments and remote monitoring for the assessment and management of their condition, as an adjunct to routine care. Methods: Using a qualitative descriptive design, participants for this study were patients with symptoms of moderate to severe depression (based on the 9-item Patient Health Questionnaire, PHQ-9), who had completed the Co-HIVE pilot. Data was collected via semi-structured interviews that were audio-recorded, transcribed clean-verbatim, and thematically analysed using the Framework Method. Results: Ten participants completed the semi-structured interviews. Participants reported experiencing more personalised care, improved health knowledge and understanding, and greater self-care, enabled by the remote monitoring technology. Additionally, participants reported virtual appointments supported the clinician–patient relationship and improved access to mental health services. Conclusions: This experience of participants with the Co-HIVE pilot indicates there is a degree of acceptance of health technologies for use with community mental healthcare. This acceptance demonstrates opportunities to innovate existing mental health services by leveraging technology.
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Serri, Evelina, Giacomo Rossi, Alessio Angorini, Lucia Biagini, Livio Galosi, and Alessandra Roncarati. "Acquisitions and evaluation of beehive parameters through an electronic system." Acta IMEKO 13, no. 2 (May 31, 2024): 1–5. http://dx.doi.org/10.21014/actaimeko.v13i2.1626.
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This study highlights the management of the hive superorganism, with the help of technology. Precision beekeeping is today a growing sector, used as apiary management strategy, based on the constant monitoring of families, minimization of consumption of resources and maximizing of productivity. Thanks to the scales and a probe placed inside each hive, we obtained data relating to winter 2021 and spring 2022, where we had flowerings 30 days in advance of the seasonality. The fluctuation of temperatures entailed the early start of laying of the queen with fresh brood to feed and heat, even when temperatures dropped drastically, having to draw on pollen and honey stocks massively. The role of the beekeeper becomes crucial, to avoid compromising the annual production and to help the survival of the colony. To know the situation inside the hive without opening it and administering an adequate nutrient supply at the right time is not always easy, and the death of beehives due to hunger is typical of the spring period. Climate changes are increasingly affecting the survival of bees and remote monitoring of beehives is becoming increasingly important to ensure their survival and productivity.
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Vorapatratorn, Surapol. "Enhancing monitoring of suspicious activities with AI-based and big data fusion." PeerJ Computer Science 10 (January 25, 2024): e1741. http://dx.doi.org/10.7717/peerj-cs.1741.
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This study provides an AI-based detection tool for the surveillance of suspicious activities using data fusion. The system leverages time, location, and specific data pertaining to individuals, objects, and vehicles associated with the agency. The study’s training data was obtained from Thailand’s military institution. The study focuses on comparing the efficiency between MySQL and Apache Hive for big data processing. According to the findings, MySQL is better suited for quick data retrieval and low storage capacity, while Hive demonstrates higher scalabilities for larger datasets. Furthermore, the study explores the practical utilization of web applications interfaces, enabling real-time display, analysis, and identification suspicious activity results. The web application, built with NuxtJS and MySQL, includes statistics charts and maps that show the status of suspicious items, cars, and people, as well as data filtering options. The system utilizes machine-learning algorithms to train the suspicious identification model, with the best-performing algorithms being the decision tree, reaching 98.867% classification accuracy.
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Cecchi, Stefania, Susanna Spinsante, Alessandro Terenzi, and Simone Orcioni. "A Smart Sensor-Based Measurement System for Advanced Bee Hive Monitoring." Sensors 20, no. 9 (May 10, 2020): 2726. http://dx.doi.org/10.3390/s20092726.
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The widespread decline of honey bee (Apis mellifera L.) colonies registered in recent years has raised great attention to the need of gathering deeper knowledge about this phenomenon, by observing the colonies’ activity to identify possible causes, and design corresponding countermeasures. In fact, honey bees have well-known positive effects on both the environment and human life, and their preservation becomes critical not only for ecological reasons, but also for the social and economic development of rural communities. Smart sensor systems are being developed for real-time and long-term measurement of relevant parameters related to beehive conditions, such as the hive weight, sounds emitted by the bees, temperature, humidity, and CO 2 inside the beehive, as well as weather conditions outside. This paper presents a multisensor platform designed to measure the aforementioned parameters from beehives deployed in the field, and shows how the fusion of different sensor measurements may provide insights on the status of the colony, its interaction with the surrounding environment, and the influence of climatic conditions.
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Struye, M. H., H. J. Mortier, G. Arnold, C. Miniggio, and R. Borneck. "Microprocessor-controlled monitoring of honeybee flight activity at the hive entrance." Apidologie 25, no. 4 (1994): 384–95. http://dx.doi.org/10.1051/apido:19940405.
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Che Ali, M. A. A., B. Ilias, N. Abdul Rahim, S. A. Abdul Shukor, A. H. Adom, and M. A. H. Saad. "A Review on the Stingless Beehive Conditions and Parameters Monitoring using IoT and Machine Learning." Journal of Physics: Conference Series 2107, no. 1 (November 1, 2021): 012040. http://dx.doi.org/10.1088/1742-6596/2107/1/012040.
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Abstract One of the stingless bee types named Heterotrigona Itama are widespread in the tropics and subtropics especially in Malaysia. Due to its excellent nutritional content, stingless bee honey has gained favour in recent years. According to some studies, stingless bee honey has been used to cure eye infections, open wounds, diabetes, hypertension, and a variety of other diseases. Additionally, this stingless bee is non-venomous and smaller in size than common bees. Nevertheless, beekeepers may encounter a number of obstacles that may result in colony failure and under-production. These problems can be attributed to a variety of factors such as surrounding temperature, surrounding humidity and predators. Numerous stingless bee colonies and other bee species lost in 2006 due to Colony Collapse Disorder as a result of this problem. Therefore, this article will review previous research on optimizing stingless beehive conditions via the use of the Internet of Things (IoT) and machine learning to minimise this issue. To begin, a review of existing research on the characteristics of stingless bees, particularly the Heterotrigona Itama species, has been conducted to understand the natural habitat of Heterotrigona Itama. Following that, the articles on colony division was reviewed in order to transition the colony from the conventional hive to the artificial hive which also reviewed its design from the past article to simplify the sensors installation, IoT monitoring system and honey harvesting. Then, the prior article on sensors and IoT deployment was examined to monitor and analysis the data online without disturbing the colony activity inside the beehives. Finally, the article on the application of machine learning with the beehive dataset was reviewed the most precise and accurate machine learning method to predict the existence of bee activity in the hives and the future condition of beehive.
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Kulyukin, Vladimir, Anastasiia Tkachenko, Kristoffer Price, William Meikle, and Milagra Weiss. "Integration of Scales and Cameras in Nondisruptive Electronic Beehive Monitoring: On the Within-Day Relationship of Hive Weight and Traffic in Honeybee (Apis mellifera) Colonies in Langstroth Hives in Tucson, Arizona, USA." Sensors 22, no. 13 (June 25, 2022): 4824. http://dx.doi.org/10.3390/s22134824.
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The relationship between beehive weight and traffic is a fundamental open research problem for electronic beehive monitoring and digital apiculture, because weight and traffic affect many aspects of honeybee (Apis mellifera) colony dynamics. An investigation of this relationship was conducted with a nondisruptive two-sensor (scale and camera) system on the weight and video data collected on six Apis mellifera colonies in Langstroth hives at the USDA-ARS Carl Hayden Bee Research Center in Tucson, Arizona, USA, from 15 May to 15 August 2021. Three hives had positive and two hives had negative correlations between weight and traffic. In one hive, weight and traffic were uncorrelated. The strength of the correlation between weight and traffic was stronger for longer time intervals. The traffic spread and mean, when taken separately, did not affect the correlation between weight and traffic more significantly than the exact traffic counts from videos. Lateral traffic did not have a significant impact on weight.
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Vijaykumar, Pashte Vrushali, and Patil Chidanand Shivshankar. "Monitoring on impact of insecticides on mortality of honey bees (Apis mellifera L.) in front of beehives." Journal of Applied and Natural Science 9, no. 2 (June 1, 2017): 905–11. http://dx.doi.org/10.31018/jans.v9i2.1296.
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The present study investigated effect of pesticide usage and public awareness on honey bee mortality. The experiments were conducted at three different sites at Maharashtra, India with domesticated bee hives of Apis mellifera L. The maximum bee mortality during 51st week of 2012-13 (1559.10 bees/hive/week) clearly indicated towards the direct and indirect effect of insecticides in general at study site I (Case I). Similar experiments were repeated at other two different sites during 2013-14. Farmers (Case II and III) were aware of beekeeping and ill effects of pesticides. Farmers followed some precautionary measures to combat with the bad effect of insecticides on bees. As a result there was less mortality of bees. The experiments revealed that farmers should be aware of bee conservation and precautionary measures to combat with the bad effect of insecticides on bees.
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Calderón-Fallas, Rafael A., Luis A. Sánchez-Chaves, and Paola Hernández-Ching. "Strategies for detection and monitoring of the small hive beetle (Aethina tumida) in Africanized honeybee colonies in Costa Rica." Ciencias Veterinarias 42, no. 1 (July 1, 2024): 1–8. http://dx.doi.org/10.15359/rcv.42-1.2.
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The small hive beetle (SHB), Aethina tumida, is a pest of honeybee (Apis mellifera) colonies. Endemic to sub-Saharan Africa, this pest has been reported in Africanized honeybee (AHB) colonies in North, Central, and South America. Specifically in Central America, it was first found in El Salvador in 2013, in Nicaragua in March 2014, and in Guatemala in August 2020. In Nicaragua, SHB was confirmed in AHB colonies in San Juan del Sur, Department of Rivas, approximately eight kilometers north of the Costa Rican border, which increased the risk of entry to this country. After SHB was confirmed in Nicaragua, a sentinel apiary with four beehives was established close to the border in Santa Cecilia, La Cruz, province of Guanacaste, Costa Rica. In addition, epidemiological surveillance was conducted in the main beekeeping areas in the country (2015-2022) to determine SHB’s possible distribution. Hives were monitored visually by examining all individual honeycombs, hive covers, and bottom boards. Furthermore, training was offered to beekeepers such as workshops and fieldwork, and training materials were distributed such as brochures focused on SHB recognition and identification and methods for colony inspection. SHB was detected and confirmed in the sentinel apiary in August 2015 in La Cruz, Guanacaste, Costa Rica, where only adult beetles were detected inside AHB colonies. To date, in collaboration with trained beekeepers, SHB has been found in different commercial apiaries, mainly in the province of Guanacaste. In conclusion, implementing strategies to detect and monitor SHB, as it spreads to new countries or beekeeping areas, requires implementing sentinel apiaries, developing epidemiological surveillance, and providing training activities for beekeepers, as demonstrated in the case in Costa Rica.
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Andrijević, Nebojša, Vlada Urošević, Branko Arsić, Dejana Herceg, and Branko Savić. "IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm." Electronics 11, no. 5 (March 3, 2022): 783. http://dx.doi.org/10.3390/electronics11050783.
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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.
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Rivera-Gomis, Jorge, Ales Gregorc, Andrea Maroni Ponti, Francesco Artese, Gertruida Zowitsky, and Giovanni Formato. "Monitoring of Small Hive Beetle (Aethina Tumida Murray) in Calabria (Italy) from 2014 to 2016: Practical Identification Methods." Journal of Apicultural Science 61, no. 2 (December 1, 2017): 257–62. http://dx.doi.org/10.1515/jas-2017-0022.
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Abstract The Small Hive Beetle (SHB), Aethina tumida, is an invasive pest of honey bee colonies that causes significant damage to the beekeeping sector. SHB was detected in southern Italy (EU) in 2014 and despite adopted eradication measures, is still present there. After three years of observations of SHB in Calabria (2014-2016), we provide here some practical tips for improving control measures. A new time-saving colony examination method, including the use of an internal divider reduced the time needed for hive inspections by 31.86 % on average. Prioritizating the inspection of pollen and honey combs rather than brood combs is advised. Sentinel apiaries with no more than five colonies without supers are suggested for each beekeeping location in order to attract and to monitor the early appearance of SHB. The use of these methods will enable early detection and prompt control measures application before this destructive pest can spread in the region.
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Guruprasad, Shreyas M., and Benjamin Leiding. "BeeOpen—An Open Data Sharing Ecosystem for Apiculture." Agriculture 14, no. 3 (March 14, 2024): 470. http://dx.doi.org/10.3390/agriculture14030470.
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The digital transformation of apiculture initially encompasses Internet of Things (IoT) systems, incorporating sensor technologies to capture and transmit bee-centric data. Subsequently, data analysis assumes a vital role by establishing correlations between the collected data and the biological conditions of beehives, often leveraging artificial intelligence (AI) approaches. The field of precision bee monitoring has witnessed a surge in the collection of large volumes of diverse data, ranging from the hive weight and temperature to health status, queen bee presence, pests, and overall hive activity. Further, these datasets’ heterogeneous nature and lack of standardization present challenges in applying machine learning techniques directly to extract valuable insights. To address this issue, the envisioned ecosystem serves as an open and collaborative information platform, facilitating the exchange and utilization of bee monitoring datasets. The data storage architecture can process a large variety of data at high frequency, e.g., images, videos, audio, and time series data. The platform serves as a repository, providing crucial information about the condition of beehives, health assessments, pest attacks, swarming patterns, and other relevant data. Notably, this information portal is managed through a citizen scientist initiative. By consolidating data from various sources, including beekeepers, researchers, and monitoring systems, the platform offers a holistic view of the bee population’s status in any given area.
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"AI-Powered Hive Monitoring System For Varroa Mite Detection And Bee Health Management." IOSR Journal of Environmental Science Toxicology and Food Technology 18, no. 11 (November 2024): 23–26. http://dx.doi.org/10.9790/2402-1811022326.
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This paper proposes an innovative AI-powered hive monitoring system designed to address the critical issue of Varroa destructor mite infestations in honey bee colonies. The Varroa mite poses a significant threat to global bee populations, weakening bees, transmitting diseases, and potentially leading to colony collapse. Traditional control methods, such as chemical treatments, often have adverse effects on bee health and the environment. Our proposed system leverages advanced technologies, including machine learning, computer vision, and sensor networks, to provide a more sustainable and effective solution for mitigating the impact of Varroa mites on bee populations.
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Jani, Muhammad Mulhim Md, Muhammad Asraf Hairuddin, Hajar Ja’afar, Ilham Rustam, Ali Abd Almisreb, and Nur Dalila Khirul Ashar. "Real-Time IoT-Blynk Application for Log Hive Weight Monitoring in Stingless Bees." Instrumentation Mesure Métrologie 23, no. 2 (April 25, 2024): 93–102. http://dx.doi.org/10.18280/i2m.230202.
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Setiawan, Anang, Tun Susdiyanti, and Kustin Bintani Meiganati. "PRODUKTIFITAS LEBAH Trigona sp. PADA BERBAGAI TEKNIK BUDIDAYA DI DESA NAYAGATI KECAMATAN LEUWIDAMAR KABUPATEN LEBAK." Jurnal Nusa Sylva 21, no. 1 (June 29, 2021): 26. http://dx.doi.org/10.31938/jns.v21i1.318.
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Non-Timber Forest Products (HHBK) have been used by communities around the forest. Apart from being easy to obtain and not requiring complicated technology to get it, it also has high economic value. It is believed that the existence of NTFPs is most closely related to the interests of the community, especially the people living around the forest, in fulfilling their daily needs. One of the NTFPs products is honey bee which has high economic and utility value. This study aims to determine the bee cultivation technique of Trigona sp. which produces the highest hive weight and bee product. This research is located in Bulakan Village, Nayagati Village, Leuwidamar District, Lebak Regency. The research was conducted for 3 (three) months from August to December 2019. The research method was by observing the weight of the hive and yield of bee products, and analyzed descriptively. The results showed that the bee cultivation technique that produced the highest productivity in terms of hive weight yield, honey, propolis and brood bee was the cage technique, and the factors that caused differences in productivity were the source of feed and the presence of predators. In the cultivation of bees Trigona sp. The source of feed is very important, besides the need for monitoring every day to avoid predators.
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Gil-Lebrero, Sergio, Francisco Javier Navas González, Victoria Gámiz López, Francisco Javier Quiles Latorre, and José Manuel Flores Serrano. "Regulation of Microclimatic Conditions inside Native Beehives and Its Relationship with Climate in Southern Spain." Sustainability 12, no. 16 (August 10, 2020): 6431. http://dx.doi.org/10.3390/su12166431.
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In this study, the Wbee Sensor System was used to record data from 10 Iberian beehives for two years in southern Spain. These data were used to identify potential conditioning climatic factors of the internal regulatory behavior of the hive and its weight. Categorical principal components analysis (CATPCA) was used to determine the minimum number of those factors able to capture the maximum percentage of variability in the data recorded. Then, categorical regression (CATREG) was used to select the factors that were linearly related to hive internal humidity, temperature and weight to issue predictive regression equations in Iberian bees. Average relative humidity values of 51.7% ± 10.4 and 54.2% ± 11.7 were reported for humidity in the brood nest and in the food area, while average temperatures were 34.3 °C ± 1.5 in the brood nest and 29.9 °C ± 5.8 in the food area. Average beehive weight was 38.2 kg ± 13.6. Some of our data, especially those related to humidity, contrast with previously published results for other studies about bees from Central and northern Europe. Conclusively, certain combinations of climatic factors may condition within hive humidity, temperature and hive weight. Southern Iberian honeybees’ brood nest humidity regulatory capacity could be lower than brood nest thermoregulatory capacity, maintaining values close to 34 °C, even in dry conditions.
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Bodescu, Dan, George Ungureanu, Radu Adrian Moraru, Ioan Gabriel Sandu, and Costica Bejinariu. "Monitoring the Anthropogenic Toxicity of Spontaneous Flora in Neamt County through Studies of the Honey Bee Chemical Characteristics." Revista de Chimie 69, no. 8 (September 15, 2018): 2150–59. http://dx.doi.org/10.37358/rc.18.8.6490.
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The products of honey bees can be used as indicators and monitors of a variety of environmental pollutants because of the bees� ability to collect materials that reflect their immediate environmental conditions. Human activities produce more waste and administrate the pesticides, the amounts and toxicity of which often exceed the environment�s homeostatic capacity to cleanse itself, and this is constantly transforming due to intensive agricultural practices necessary to increase food production as human population grows. The main sources for contamination of honey with heavy metals are represented by placing hives near urban areas with heavy car traffic, or industrialized areas and the use on the entire circuit of production, objects or containers made of materials unsuitable (unacceptable). For that reason regular monitoring of the environment is so important. Honey bees, thanks to their morphological features, and also bee products are regarded as good indicators of environmental pollution by toxic substances, be these heavy metals, radioactive elements or persistent organic pollutants such as pesticides. Consequently, it is important to estimate the environmental fate and Eco toxicological effects of these different xenobiotic. Honey bees (Apis mellifera L.) have been used as biological indicators of Plant Protection Products (PPPs) in two intensely cultivated in areas of Neamt County, Romania. This area is representative for the pre-mountain and mountain zone of Romania. The stratified sample has been face-to-face interviewed in 2016 regarding the data from the year 2015. The total consumption specific for the honey production was about 628 MJ hive-1, and the energy output reached 235 MJ hive-1, determining an energy productivity of 0.030 kg MJ-1 and an energy use efficiency of 0.37. Specific energy amounted 33.3MJ kg-1 due to the inefficiency of traveling during the apiaries movements and the inappropriate correlation between the apiaries size and the zonal melliferous potential. In this paper available literature data and information on the morphological features of the honey bee, the utilization of the honey bee and its products as indicators of environmental pollution, and a historical outline of some of the legislation relating to beekeeping have been critically compared and discussed.
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Schaumann, Finja, Niclas Norrström, Mats Niklasson, and Sonja Leidenberger. "Ecological comparison of native (Apis mellifera mellifera) and hybrid (Buckfast) honeybee drones in southwestern Sweden indicates local adaptation." PLOS ONE 19, no. 8 (August 13, 2024): e0308831. http://dx.doi.org/10.1371/journal.pone.0308831.
Full textAbstract:
Honeybee drones’ only known task is to mate with a virgin queen. Apart from their mating behaviour, their ecology has been little studied, especially in comparison to honeybee females. Previous knowledge is primarily based on short-term direct observations at single experimental hives, rarely, if ever, addressing the effect of drones’ genetic origin. Here, Radio Frequency Identification Technology was utilised to gather drone and worker bee lifetime data of Apis mellifera mellifera and Apis mellifera x (hybrid Buckfast) colonies over one mating season (spring and summer) with the ultimate goal to investigate differences at subspecies level. This technique enabled continuous monitoring of tagged bees at the hive entrance and recording of individuals’ movement directions. The results confirmed that spring-born drones survive longer than summer-born drones and that they generally live longer than worker bees. Drones’ peak activity occurred in the afternoon while worker bees showed more even activity levels throughout the day. Earlier orientation flights than usually reported for drones were observed. In summer, mating flights were practiced before reaching sexual maturity (at 12 days of age). Differences were found between Apis m. mellifera and Buckfast drones, where Apis m. mellifera showed later drone production in spring, but significantly earlier first activities outside the hive in summer and a later peak in diurnal activity. Additionally, Apis m. mellifera flew more in higher light intensities and windy conditions and performed significantly longer flights than Buckfast drones. The observed differences in drone ecology indicate the existence of a local adaptation of the native subspecies Apis m. mellifera to environmental conditions in southwestern Sweden.
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Yuan, Ying, and Lejin Xu. "Multidimensional Data Analysis of Ambient Air Quality Based on Apache Kylin." Journal of Physics: Conference Series 2555, no. 1 (July 1, 2023): 012001. http://dx.doi.org/10.1088/1742-6596/2555/1/012001.
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Abstract While the environmental monitoring department increases the number of monitoring points and the frequency of monitoring, it will also bring about a surge in the amount of monitoring data and computational response events. However, the traditional data statistics methods based on relational databases are ineffective in the face of huge environmental monitoring data. Aiming at ambient air quality data analysis, this paper uses Hadoop, Hive, Kylin, and other tools to build a multi-dimensional analysis platform for ambient air quality big data in a distributed environment, which realizes the unified storage, calculation, and analysis of ambient air quality monitoring data. Compared with the traditional relational database statistical analysis scheme, the proposed solution significantly improves the efficiency of statistical analysis of ambient air quality data under the condition of large data. The response time is shortened by 98%, reaching the sub-second level.
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Babic, Z., R. Pilipovic, V. Risojevic, and G. Mirjanic. "POLLEN BEARING HONEY BEE DETECTION IN HIVE ENTRANCE VIDEO RECORDED BY REMOTE EMBEDDED SYSTEM FOR POLLINATION MONITORING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 51–57. http://dx.doi.org/10.5194/isprsannals-iii-7-51-2016.
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Honey bees have crucial role in pollination across the world. This paper presents a simple, non-invasive, system for pollen bearing honey bee detection in surveillance video obtained at the entrance of a hive. The proposed system can be used as a part of a more complex system for tracking and counting of honey bees with remote pollination monitoring as a final goal. The proposed method is executed in real time on embedded systems co-located with a hive. Background subtraction, color segmentation and morphology methods are used for segmentation of honey bees. Classification in two classes, pollen bearing honey bees and honey bees that do not have pollen load, is performed using nearest mean classifier, with a simple descriptor consisting of color variance and eccentricity features. On in-house data set we achieved correct classification rate of 88.7% with 50 training images per class. We show that the obtained classification results are not far behind from the results of state-of-the-art image classification methods. That favors the proposed method, particularly having in mind that real time video transmission to remote high performance computing workstation is still an issue, and transfer of obtained parameters of pollination process is much easier.