1. An AI-Based Digital Scanner for Varroa destructor Detection in Beekeeping.
- Author
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Scutaru, Daniela, Bergonzoli, Simone, Costa, Corrado, Violino, Simona, Costa, Cecilia, Albertazzi, Sergio, Capano, Vittorio, Kostić, Marko M., and Scarfone, Antonio
- Subjects
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VARROA destructor , *POLLINATION by bees , *HONEYBEES , *ARTIFICIAL intelligence , *INSPECTION & review , *BEEKEEPING - Abstract
Simple Summary: A major threat to honey bees is the Varroa destructor mite, a parasite that feeds on bee fat bodies and transmits viruses, leading to significant colony losses. Detecting the level of Varroa infestation in the apiary is crucial for defining appropriate intervention strategies and preventing irreparable damage to the colonies. Traditional methods based on manual counting are time-consuming and require meticulous attention. In this study, we tested an AI-based portable scanner for Varroa destructor detection. The device operates through image analysis of a sticky sheet placed under the beehive for several days, capturing the Varroa mites that naturally fall. Over 17 weeks, the scanner was tested with sheets from five beehives each week, assessing the accuracy, reliability, and speed of the method compared to conventional human visual inspection. Results show that the system can consistently repeat measurements with high precision, with an error rate in detecting Varroa mites consistently below 1% when there are more than 10 mites per sheet. Given its repeatability and reliability, the device can be considered a valuable tool for beekeepers and scientists, offering the opportunity to monitor many beehives in a short time. 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 Varroa destructor 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 (R2 ≥ 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. [ABSTRACT FROM AUTHOR]
- Published
- 2025
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