A framework for better sensor-based beehive health monitoring

Zaman, Asaduz; Dorin, Alan

doi:10.1016/j.compag.2023.107906

Cited by 16 publications

(1 citation statement)

References 93 publications

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“…However, during the spring, in the run up to swarming, such inspections are a necessity for good beekeeping. The ability of the beekeeper to monitor the health of their colony has aided the understanding of these challenges and the use of electronic monitoring aids, such as hive scales and temperature sensors [16][17][18][19][20][21][22][23][24]. The mass of the hive provides important information on the size and activity of the colony.…”

Section: Introductionmentioning

confidence: 99%

Winter Carbon Dioxide Measurement in Honeybee Hives

Newton,

Chamberlain,

McVeigh

et al. 2024

Applied Sciences

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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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Section: Introductionmentioning

confidence: 99%

Winter Carbon Dioxide Measurement in Honeybee Hives

Newton,

Chamberlain,

McVeigh

et al. 2024

Applied Sciences

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Recognizing Beehives’ Health Abnormalities Based on Mobile Net Deep Learning Model

Torky

Nasr

Hassanien

2023

Int J Comput Intell Syst

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Monitoring beehive health is a major area of interest within the field of honeybee economy. Ensuring beehives are free of problems such as Varroa destructors and hive beetles, ant problems, and missing queen represents an important challenge in the honeybee industry. Therefore, it is mandatory to have untraditional ways to diagnose these types of honeybee attacks. Artificial Intelligence (AI), computer vision, and the Internet of Things (IoT) can be integrated to develop smart systems for developing warning, prediction, and recognition systems to analyze beehives' health impacts, and conditions as well as monitor bees' behaviors and the environmental conditions inside/outside beehives. In this paper, a deep learning methodology is proposed to recognize the beehives' health abnormalities, Varroa destructors, hive beetles, ant problems, and missing queens. A novel version of the MobileNet model is developed by modifying the front layers of the mobile net model for performing the features selection phase. Three optimization algorithms are utilized and tested on a benchmark dataset of beehives, Adam optimizer, Nesterov-accelerated Adam (Nadam) optimizer, and Stochastic gradient descent (SGD) for selecting the most important features to recognize the three beehive health abnormalities. The implementation and validation results proved the efficiency of the Mobile Net using Adam optimizer in classifying beehives according to the three beehive health abnormalities (Varroa destructor and hive beetles, ant problems, and missing queen) where the model achieved testing accuracy of 95% and testing loss of 35%. In addition, the validation and comparison results confirmed the superiority of Mobile Net using ADAM optimizer in recognizing beehive health abnormalities compared to four deep learning models, Shuffle Net, Resent 50, VGG-19, and Google Net.

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