Diagnosis of Varroosis Based on Bee Brood Samples Testing with Use of Semiconductor Gas Sensors

Bąk, Beata; Wilk, Jakub; Artiemjew, Piotr; Wilde, J.; Siuda, M.

doi:10.3390/s20144014

Cited by 10 publications

(10 citation statements)

References 33 publications

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“…The Figaro 6-sensor TGS device we used has so far given satisfactory results in Varroa diagnostics, both in the laboratory (ref. [ 12 ] and in the field conditions [ 11 ]. We also successfully detected colonies on MYPGP media P.l.…”

Section: Discussionmentioning

confidence: 99%

“…These devices have found applications in the diagnosis of diseases in plants [ 8 ], animals [ 9 ], and humans [ 10 ]. In the case of bee diseases, an array of six solid-state gas sensors can distinguish bee colonies heavily infected with the Varroa destructor parasite from healthy colonies [ 11 ] and can diagnose Varroa by examining brood samples infected with this mite [ 12 ]. The effectiveness in detecting American foulbrood in bee colonies by the multi-sensor device in preliminary studies was confirmed by a team of scientists from Australia [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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In-Field Detection of American Foulbrood (AFB) by Electric Nose Using Classical Classification Techniques and Sequential Neural Networks

Bąk

Szkoła

Wilk

et al. 2022

Sensors

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American foulbrood is a dangerous bee disease that attacks the sealed brood. It quickly leads to the death of bee colonies. Efficient diagnosis of this disease is essential. As specific odours are produced when larvae rot, it was investigated whether an electronic nose can distinguish between colonies affected by American foulbrood and healthy ones. The experiment was conducted in an apiary with 18 bee families, 9 of which showed symptoms of the disease confirmed by laboratory diagnostics. Three units of the Beesensor V.2 device based on an array of six semiconductor TGS gas sensors, manufactured by Figaro, were tested. Each copy of the device was tested in all bee colonies: sick and healthy. The measurement session per bee colony lasted 40 min and yielded results from four 10 min measurements. One 10-min measurement consisted of a 5 min regeneration phase and a 5 min object-measurement phase. For the experiments, we used both classical classification methods such as k-nearest neighbour, Naive Bayes, Support Vector Machine, discretized logistic regression, random forests, and committee of classifiers, that is, methods based on extracted representative data fragments. We also used methods based on the entire 600 s series, in this study of sequential neural networks. We considered, in this study, six options for data preparation as part of the transformation of data series into representative results. Among others, we used single stabilised sensor readings as well as average values from stable areas. For verifying the quality of the classical classifiers, we used the 25-fold train-and-test method. The effectiveness of the tested methods reached a threshold of 75 per cent, with results stable between 65 and 70 per cent. As an element to confirm the possibility of class separation using an artificial nose, we used applied visualisations of classes. It is clear from the experiments conducted that the artificial nose tested has practical potential. Our experiments show that the approach to the problem under study by sequential network learning on a sequence of data is comparable to the best classical methods based on discrete data samples. The results of the experiment showed that the Beesensor V.2 along with properly selected classification techniques can become a tool to facilitate rapid diagnosis of American foulbrood under field conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In-Field Detection of American Foulbrood (AFB) by Electric Nose Using Classical Classification Techniques and Sequential Neural Networks

Bąk

Szkoła

Wilk

et al. 2022

Sensors

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“…Improvements to reduce the number of hive visitations and negative impacts upon the colonies can be only be a positive step. Currently, the use of gas sensors (otherwise known as electronic noses, capable of identifying complex odours (Bąk et al 2020)) and video for detecting mite presence in hives are being explored. At present, these sensors are being tested for their ability to discriminate between healthy and mite-infested colony odours (Bąk et al 2020;Szczurek et al 2020a;Szczurek et al 2020b;Konig 2021), and in most cases these can be inserted into a hive and left to carry out continuous measurements that can be accessed remotely (Szczurek et al 2020a;Szczurek et al 2020b;Konig 2021).…”

Section: Remote Varroa Monitoring Techniquesmentioning

confidence: 99%

Varroa destructor mites regularly generate ultra-short, high magnitude vibrational pulses.

Hall¹,

Bencsik²,

Newton³

et al. 2022

entomologia

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The ectoparasitic mite Varroa destructor is considered one of the greatest threats to the honeybee Apis mellifera. To successfully manage mite populations residing in the colony, beekeepers must stay informed of infestation levels in their apiaries. The remote, non-destructive detection of Varroa mites in honeybee hives would therefore be highly desirable.Here we show that an ultra-sensitive (1000 mV/g) accelerometer can detect vibrational waveforms originating from one individual mite. We further focus on a commonly observed pulsing behaviour never before described, characterising its physical features, periodicity and strength. The spectral features of the detected pulses strongly depend on the substrate on which they are produced. The characteristics of the vibrational pulse, particularly its repeatability and strength, indicate that mite vibrations could be successfully detected in a fully populated honeybee hive. These features, combined with the remarkably high varroa muscular power output (up to 810nW) indicate that this pulse may be functional for the mite. Our results uncover an exciting novel behaviour and provide a foundation for the remote detection of mites in beehives using vibration capture.

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“…Table 1. The characteristics of semiconductor gas sensors, which were used in the multi-sensor array ( [9], https://www. figaro.co.jp/en (accessed on 17 July 2021)).…”

Section: Construction and Mechanism Of The Mca-8 Device's Operationmentioning

confidence: 99%

“…The most dangerous disease that plagues the honey bee is varroosis. It was shown that a matrix of six semiconductor gas sensors can distinguish bee colonies heavily infected with Varroa destructor from healthy ones [8], and this allows for the diagnosis of varroosis based on the examination of brood samples infected with this mite [9]. Another dangerous disease of bees is American foulbrood (AFB), caused by the Paenibacillus larvae larvae L. bacterium.…”

Section: Introductionmentioning

confidence: 99%

Recording the Presence of Peanibacillus larvae larvae Colonies on MYPGP Substrates Using a Multi-Sensor Array Based on Solid-State Gas Sensors

Bąk

Wilk

Artiemjew

et al. 2021

Sensors

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American foulbrood is a dangerous disease of bee broods found worldwide, caused by the Paenibacillus larvae larvae L. bacterium. In an experiment, the possibility of detecting colonies of this bacterium on MYPGP substrates (which contains yeast extract, Mueller-Hinton broth, glucose, K2HPO4, sodium pyruvate, and agar) was tested using a prototype of a multi-sensor recorder of the MCA-8 sensor signal with a matrix of six semiconductors: TGS 823, TGS 826, TGS 832, TGS 2600, TGS 2602, and TGS 2603 from Figaro. Two twin prototypes of the MCA-8 measurement device, M1 and M2, were used in the study. Each prototype was attached to two laboratory test chambers: a wooden one and a polystyrene one. For the experiment, the strain used was P. l. larvae ATCC 9545, ERIC I. On MYPGP medium, often used for laboratory diagnosis of American foulbrood, this bacterium produces small, transparent, smooth, and shiny colonies. Gas samples from over culture media of one- and two-day-old foulbrood P. l. larvae (with no colonies visible to the naked eye) and from over culture media older than 2 days (with visible bacterial colonies) were examined. In addition, the air from empty chambers was tested. The measurement time was 20 min, including a 10-min testing exposure phase and a 10-min sensor regeneration phase. The results were analyzed in two variants: without baseline correction and with baseline correction. We tested 14 classifiers and found that a prototype of a multi-sensor recorder of the MCA-8 sensor signal was capable of detecting colonies of P. l. larvae on MYPGP substrate with a 97% efficiency and could distinguish between MYPGP substrates with 1–2 days of culture, and substrates with older cultures. The efficacy of copies of the prototypes M1 and M2 was shown to differ slightly. The weighted method with Canberra metrics (Canberra.811) and kNN with Canberra and Manhattan metrics (Canberra. 1nn and manhattan.1nn) proved to be the most effective classifiers.

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Diagnosis of Varroosis Based on Bee Brood Samples Testing with Use of Semiconductor Gas Sensors

Cited by 10 publications

References 33 publications

In-Field Detection of American Foulbrood (AFB) by Electric Nose Using Classical Classification Techniques and Sequential Neural Networks

In-Field Detection of American Foulbrood (AFB) by Electric Nose Using Classical Classification Techniques and Sequential Neural Networks

Varroa destructor mites regularly generate ultra-short, high magnitude vibrational pulses.

Recording the Presence of Peanibacillus larvae larvae Colonies on MYPGP Substrates Using a Multi-Sensor Array Based on Solid-State Gas Sensors

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