Piotr Artiemjew
Associate Professor
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About
Piotr Artiemjew is an Associate Professor at the University of Warmia and Mazury in Olsztyn, Poland. His research areas include machine learning applications in medical diagnostics, robotics, environmental science, and the study of bee biology. He has published numerous articles addressing topics such as the Extreme Learning Machine framework, the use of gas sensors in detecting bee diseases, and the impacts of climate variability on lagoon ecosystems.
Articles (14)
Year:
2025
Year:
2025
Year:
2025
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.
Year:
2023
Year:
2022
Year:
2022
Year:
2021
Year:
2021
Year:
2021
Year:
2021
Year:
2020
Year:
2020
Year:
2020
Year:
2020
Collaborators (8)
Marek Kruk
University of Warmia and Mazury in Olsztyn
Jacek Rapinski
University of Warmia and Mazury in Olsztyn
Andrzej Zajac
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Szymon Nitkiewicz
Assistant Profesor
University of Warmia and Mazury in Olsztyn
Manuel Ojeda-Aciego
Full Professor
University of Malaga
Robert Barański
lecturer
AGH University of Science and Technology
Jerzy Wilde
University of Warmia and Mazury in Olsztyn
Irina Perfilieva
Professor
Cracow University of Technology
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