An AI-Based Digital Scanner for <i>Varroa destructor</i> Detection in Beekeeping
Beekeeping is a crucial agricultural practice that significantly enhances environmental health and food production through effective pollination by honey bees. However, honey bees face numerous threats, including exotic parasites, large-scale transportation, and common agricultural practices that ma...
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MDPI AG
2025-01-01
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| Series: | Insects |
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| Online Access: | https://www.mdpi.com/2075-4450/16/1/75 |
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| author | Daniela Scutaru Simone Bergonzoli Corrado Costa Simona Violino Cecilia Costa Sergio Albertazzi Vittorio Capano Marko M. Kostić Antonio Scarfone |
| author_facet | Daniela Scutaru Simone Bergonzoli Corrado Costa Simona Violino Cecilia Costa Sergio Albertazzi Vittorio Capano Marko M. Kostić Antonio Scarfone |
| author_sort | Daniela Scutaru |
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| description | Beekeeping is a crucial agricultural practice that significantly enhances environmental health and food production through effective pollination by honey bees. However, honey bees face numerous threats, including exotic parasites, large-scale transportation, and common agricultural practices that may increase the risk of parasite and pathogen transmission. A major threat is the <i>Varroa destructor</i> mite, which feeds on honey bee fat bodies and transmits viruses, leading to significant colony losses. Detecting the parasite and defining the intervention thresholds for effective treatment is a difficult and time-consuming task; different detection methods exist, but they are mainly based on human eye observations, resulting in low accuracy. This study introduces a digital portable scanner coupled with an AI algorithm (BeeVS) used to detect Varroa mites. The device works through image analysis of a sticky sheet previously placed under the beehive for some days, intercepting the Varroa mites that naturally fall. In this study, the scanner was tested for 17 weeks, receiving sheets from 5 beehives every week, and checking the accuracy, reliability, and speed of the method compared to conventional human visual inspection. The results highlighted the high repeatability of the measurements (R<sup>2</sup> ≥ 0.998) and the high accuracy of the BeeVS device; when at least 10 mites per sheet were present, the device showed a cumulative percentage error below 1%, compared to approximately 20% for human visual observation. Given its repeatability and reliability, the device can be considered a valid tool for beekeepers and scientists, offering the opportunity to monitor many beehives in a short time, unlike visual counting, which is done on a sample basis. |
| format | Article |
| id | doaj-art-8c61d83c20b74bf5b74ee79aa78e5876 |
| institution | Kabale University |
| issn | 2075-4450 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Insects |
| spelling | doaj-art-8c61d83c20b74bf5b74ee79aa78e58762025-01-24T13:35:48ZengMDPI AGInsects2075-44502025-01-011617510.3390/insects16010075An AI-Based Digital Scanner for <i>Varroa destructor</i> Detection in BeekeepingDaniela Scutaru0Simone Bergonzoli1Corrado Costa2Simona Violino3Cecilia Costa4Sergio Albertazzi5Vittorio Capano6Marko M. Kostić7Antonio Scarfone8Council for Agricultural Research and Economics, Research Centre for Engineering and Agro-Food Processing, Via della Pascolare 16, 00015 Monterotondo, ItalyCouncil for Agricultural Research and Economics, Research Centre for Engineering and Agro-Food Processing, Via della Pascolare 16, 00015 Monterotondo, ItalyCouncil for Agricultural Research and Economics, Research Centre for Engineering and Agro-Food Processing, Via della Pascolare 16, 00015 Monterotondo, ItalyCouncil for Agricultural Research and Economics, Research Centre for Engineering and Agro-Food Processing, Via della Pascolare 16, 00015 Monterotondo, ItalyCouncil for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Via di Corticella, 133, 40128 Bologna, ItalyCouncil for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Via di Corticella, 133, 40128 Bologna, ItalyCouncil for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Via di Corticella, 133, 40128 Bologna, ItalyFaculty of Agriculture, University of Novi Sad, Trg. D. Obradovića 8, 21000 Novi Sad, SerbiaCouncil for Agricultural Research and Economics, Research Centre for Engineering and Agro-Food Processing, Via della Pascolare 16, 00015 Monterotondo, ItalyBeekeeping is a crucial agricultural practice that significantly enhances environmental health and food production through effective pollination by honey bees. However, honey bees face numerous threats, including exotic parasites, large-scale transportation, and common agricultural practices that may increase the risk of parasite and pathogen transmission. A major threat is the <i>Varroa destructor</i> mite, which feeds on honey bee fat bodies and transmits viruses, leading to significant colony losses. Detecting the parasite and defining the intervention thresholds for effective treatment is a difficult and time-consuming task; different detection methods exist, but they are mainly based on human eye observations, resulting in low accuracy. This study introduces a digital portable scanner coupled with an AI algorithm (BeeVS) used to detect Varroa mites. The device works through image analysis of a sticky sheet previously placed under the beehive for some days, intercepting the Varroa mites that naturally fall. In this study, the scanner was tested for 17 weeks, receiving sheets from 5 beehives every week, and checking the accuracy, reliability, and speed of the method compared to conventional human visual inspection. The results highlighted the high repeatability of the measurements (R<sup>2</sup> ≥ 0.998) and the high accuracy of the BeeVS device; when at least 10 mites per sheet were present, the device showed a cumulative percentage error below 1%, compared to approximately 20% for human visual observation. Given its repeatability and reliability, the device can be considered a valid tool for beekeepers and scientists, offering the opportunity to monitor many beehives in a short time, unlike visual counting, which is done on a sample basis.https://www.mdpi.com/2075-4450/16/1/75AIneural networkbeekeeping<i>Varroa destructor</i>scanner |
| spellingShingle | Daniela Scutaru Simone Bergonzoli Corrado Costa Simona Violino Cecilia Costa Sergio Albertazzi Vittorio Capano Marko M. Kostić Antonio Scarfone An AI-Based Digital Scanner for <i>Varroa destructor</i> Detection in Beekeeping Insects AI neural network beekeeping <i>Varroa destructor</i> scanner |
| title | An AI-Based Digital Scanner for <i>Varroa destructor</i> Detection in Beekeeping |
| title_full | An AI-Based Digital Scanner for <i>Varroa destructor</i> Detection in Beekeeping |
| title_fullStr | An AI-Based Digital Scanner for <i>Varroa destructor</i> Detection in Beekeeping |
| title_full_unstemmed | An AI-Based Digital Scanner for <i>Varroa destructor</i> Detection in Beekeeping |
| title_short | An AI-Based Digital Scanner for <i>Varroa destructor</i> Detection in Beekeeping |
| title_sort | ai based digital scanner for i varroa destructor i detection in beekeeping |
| topic | AI neural network beekeeping <i>Varroa destructor</i> scanner |
| url | https://www.mdpi.com/2075-4450/16/1/75 |
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