Basic algorithms for bee hive monitoring and laser-based mite control

Chazette, Larissa; Becker, Matthias; Szczerbicka, Helena

doi:10.1109/ssci.2016.7850001

Cited by 23 publications

(27 citation statements)

References 7 publications

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“…In [ 11 ], a web-based monitoring system built upon sensors and a cloud architecture, to monitor and follow bees’ behavior, is described. In [ 12 , 13 , 14 , 15 ], different approaches for heterogeneous wireless sensor networks technologies to gather data unobtrusively from a bee hive have been described. Approaches based on computer vision have been proposed as well: in [ 16 ], a system to track bees in a 50 Hz frame rate 2D video of the hive entrance close view is presented.…”

Section: Introductionmentioning

confidence: 99%

On the Importance of the Sound Emitted by Honey Bee Hives

Terenzi

Cecchi

Spinsante

2020

Veterinary Sciences

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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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Section: Introductionmentioning

confidence: 99%

On the Importance of the Sound Emitted by Honey Bee Hives

Terenzi

Cecchi

Spinsante

2020

Veterinary Sciences

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“…Practical applications stemming from efficient bee monitoring systems and V.-mite detection systems have also been studied. The authors of [ 25 ] investigate the possibility of using a camera based bee monitoring system on the hive’s entrance to detect V.-mites and then destroy them with a focused laser beam. The authors outlined hardware and software requirements for such a system and found it feasible even if such systems have not yet been developed to a deployable state.…”

Section: Related Workmentioning

confidence: 99%

Visual Diagnosis of the Varroa Destructor Parasitic Mite in Honeybees Using Object Detector Techniques

Bilík

Kratochvíla

Ligocki

et al. 2021

Sensors

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The Varroa destructor mite is one of the most dangerous Honey Bee (Apis mellifera) parasites worldwide and the bee colonies have to be regularly monitored in order to control its spread. In this paper we present an object detector based method for health state monitoring of bee colonies. This method has the potential for online measurement and processing. In our experiment, we compare the YOLO and SSD object detectors along with the Deep SVDD anomaly detector. Based on the custom dataset with 600 ground-truth images of healthy and infected bees in various scenes, the detectors reached the highest F1 score up to 0.874 in the infected bee detection and up to 0.714 in the detection of the Varroa destructor mite itself. The results demonstrate the potential of this approach, which will be later used in the real-time computer vision based honey bee inspection system. To the best of our knowledge, this study is the first one using object detectors for the Varroa destructor mite detection on a honey bee. We expect that performance of those object detectors will enable us to inspect the health status of the honey bee colonies in real time.

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“…F1-score is a metric to evaluate models that corresponds to the harmonic mean between the precision and recall metrics [26]. Both [14] and [17] used CNN and ML algorithms to perform bee hive monitoring, aiming at the detection of mites and varroa destructor. The first study obtained an accuracy of 93% detection of varroa destructor using a training data set of 5000 artificially generated images, tested with different CNN configurations [14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Advances in Deep Learning (DL) and Computer Vision (CV) have shown potential applications in the automatic detection of bees and their respective health, such as detecting environment conditions (i.e., temperature, humidity, etc. ), detecting signs of varroa destructor, among others [11], [14]- [18]. Considering these advancements, the classification of a bee's health status can be performed through images of the specimen, using CV and DL.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Monitoring System for Assessing Bee Hive Health

et al. 2021

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Up to one third of the global food production depends on the pollination of honey bees, making them vital. This study defines a methodology to create a bee hive health monitoring system through image processing techniques. The approach consists of two models, where one performs the detection of bees in an image and the other classifies the detected bee's health. The main contribution of the defined methodology is the increased efficacy of the models, whilst maintaining the same efficiency found in the state of the art. Two databases were used to create models based on Convolutional Neural Network (CNN). The best results consist of 95% accuracy for health classification of a bee and 82% accuracy in detecting the presence of bees in an image, higher than those found in the state-of-the-art.

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Basic algorithms for bee hive monitoring and laser-based mite control

Cited by 23 publications

References 7 publications

On the Importance of the Sound Emitted by Honey Bee Hives

On the Importance of the Sound Emitted by Honey Bee Hives

Visual Diagnosis of the Varroa Destructor Parasitic Mite in Honeybees Using Object Detector Techniques

An Intelligent Monitoring System for Assessing Bee Hive Health

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