Monitoring of Varroa Infestation Rate in Beehives: A Simple AI Approach
| dc.contributor.author | Picek, Lukáš | |
| dc.contributor.author | Novozamsky, Adam | |
| dc.contributor.author | Frydrychová, Radmila C. | |
| dc.contributor.author | Zitová, Barbara | |
| dc.contributor.author | Mach, Pavel | |
| dc.date.accessioned | 2023-03-06T11:00:26Z | |
| dc.date.available | 2023-03-06T11:00:26Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract-translated | This paper addresses the monitoring of Varroa destructor infestation in Western honey bee colonies. We propose a simple approach using automatic image-based analysis of the fallout on beehive bottom boards. In contrast to the existing high-tech methods, our solution does not require extensive and expensive hardware components, just a standard smartphone. The described method has the potential to replace the time-consuming, inaccurate, and most common practice where the infestation level is evaluated manually. The underlining machine learning method combines a thresholding algorithm with a shallow CNN-VarroaNet. It provides a reliable estimate of the infestation level with a mean infestation level accuracy of 96.0% and 93.8% in the autumn and winter, respectively. Furthermore, we introduce the developed end-to-end system and its deployment into the online beekeeper's diary-ProBee-that allows users to identify and track infestation levels on bee colonies. | en |
| dc.format | 5 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | PICEK, L. NOVOZAMSKY, A. FRYDRYCHOVÁ, RC. ZITOVÁ, B. MACH, P. Monitoring of Varroa Infestation Rate in Beehives: A Simple AI Approach. In Proceedings - International Conference on Image Processing, ICIP 2022. New York: IEEE, 2022. s. 3341-3345. ISBN: 978-1-66549-620-9 , ISSN: 1522-4880 | cs |
| dc.identifier.doi | 10.1109/ICIP46576.2022.9897809 | |
| dc.identifier.isbn | 978-1-66549-620-9 | |
| dc.identifier.issn | 1522-4880 | |
| dc.identifier.obd | 43937117 | |
| dc.identifier.uri | 2-s2.0-85146732817 | |
| dc.identifier.uri | http://hdl.handle.net/11025/51653 | |
| dc.language.iso | en | en |
| dc.project.ID | SS05010008/Detekce, identifikace a monitoring živočichů pokročilými metodami počítačového vidění | cs |
| dc.publisher | IEEE | en |
| dc.relation.ispartofseries | Proceedings - International Conference on Image Processing, ICIP 2022 | en |
| dc.rights | Plný text je přístupný v rámci univerzity přihlášeným uživatelům | cs |
| dc.rights | © IEEE | en |
| dc.rights.access | restrictedAccess | en |
| dc.subject.translated | Apiculture | en |
| dc.subject.translated | Bee | en |
| dc.subject.translated | CNN | en |
| dc.subject.translated | Computer Vision | en |
| dc.subject.translated | Machine Learning | en |
| dc.subject.translated | Mite | en |
| dc.subject.translated | Varroa | en |
| dc.title | Monitoring of Varroa Infestation Rate in Beehives: A Simple AI Approach | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | ConferenceObject | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |