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

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

doi:10.3390/s21144917

Cited by 3 publications

(4 citation statements)

References 43 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our research introduces concepts that are entirely original and unprecedented in their character, offering a fresh perspective in the field. As for the usage of semiconductor gas sensors in beekeeping, thus far, the possibility of detecting dangerous bee and brood diseases with them has been analyzed [ 26 , 27 , 28 , 40 ], as well as the effectiveness in recognizing the status of worker bees [ 25 ] or honey varieties [ 41 ]. For the first time, it has been investigated whether the gas sensor matrix is able to recognize the contents of honey bee comb cells.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

The Identification of Bee Comb Cell Contents Using Semiconductor Gas Sensors

Bąk,

Wilk,

Artiemjew

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Beekeeping is an extremely difficult field of agriculture. It requires efficient management of the bee nest so that the bee colony can develop efficiently and produce as much honey and other bee products as possible. The beekeeper, therefore, must constantly monitor the contents of the bee comb. At the University of Warmia and Mazury in Olsztyn, research is being carried out to develop methods for efficient management of the apiary. One of our research goals was to test whether a gas detector (MCA-8) based on six semiconductor sensors—TGS823, TGS826, TGS832, TGS2600, TGS2602, and TGS2603 from the company FIGARO—is able to recognize the contents of bee comb cells. For this purpose, polystyrene and wooden test chambers were created, in which fragments of bee comb with different contents were placed. Gas samples were analyzed from an empty comb, a comb with sealed brood, a comb with open brood, a comb with carbohydrate food in the form of sugar syrup, and a comb with bee bread. In addition, a sample of gas from an empty chamber was tested. The results in two variants were analyzed: (1) Variant 1, the value of 270 s of sensor readings from the sample measurement (exposure phase), and (2) Variant 2, the value of 270 s of sensor readings from the sample measurement (measurement phase) with baseline correction by subtracting the last 600 s of surrounding air measurements (flushing phase). A five-time cross-validation 2 (5xCV2) test and the Monte Carlo cross-validation 25 (trained and tested 25 times) were performed. Fourteen classifiers were tested. The naive Bayes classifier (NB) proved to be the most effective method for distinguishing individual classes from others. The MCA-8 device brilliantly differentiates an empty comb from a comb with contents. It differentiates better between an empty comb and a comb with brood, with results of more than 83%. Lower class accuracy was obtained when distinguishing an empty comb from a comb with food and a comb with bee bread, with results of less than 73%. The matrix of six TGS sensors in the device shows promising versatility in distinguishing between various types of brood and food found in bee comb cells. This capability, though still developing, positions the MCA-8 device as a potentially invaluable tool for enhancing the efficiency and effectiveness of beekeepers in the future.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Air quality measurements using semiconductor gas sensors are already being carried out in many industries [ 22 , 23 , 24 ]. Excellent results were obtained in the detection of bee and brood diseases and the status of worker bees [ 25 , 26 , 27 , 28 ]. A particular application is FIGARO’s TGS series of sensors.…”

Section: Introductionmentioning

confidence: 99%

The Identification of Bee Comb Cell Contents Using Semiconductor Gas Sensors

Bąk,

Wilk,

Artiemjew

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…We also successfully detected colonies on MYPGP media P.l. larvae [ 34 ]. In this article, we presented the results of using a BeesensorV.2 device based on an array of the same six sensors to detect American foulbrood under field conditions in live bee colonies.…”

Section: Discussionmentioning

confidence: 99%

“…It must be remembered that these are field conditions. The situation is different in the case of laboratory tests, where the array of the same sensors already gave stable readings in the first and only measurement (10 min of measurement session) [ 12 , 34 , 35 ].…”

Section: Discussionmentioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract