0563135 20250121112331.420221031235959.9 85146732817 001058109503087 10.1109/ICIP46576.2022.9897809 5 s. E cav_un_epca*0564843978-1-6654-9620-92381-85493341-3345PiscatawayIEEE2022 Machine learning algorithms Costs Image processing Machine learning Frequency measurement Complexity theory cav_un_auth*0401593 Picek L. CZ cav_un_auth*0283562 Novozámský Adam UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0274385 Čapková Frydrychová Radmila BC-A ENTU - Molekulární biologie a genetika Molecular Biology and Genetics Insect Molecular Biology and Genetics Biologické centrum AV ČR, v. v. i. cav_un_auth*0101238 Zitová Barbara UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0050534 Mach P. CZ http://library.utia.cas.cz/separaty/2022/ZOI/novozamsky-0563135.pdf StrategieAV21/1 AV ČR CZ cav_un_auth*0328930 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 smart-phone. 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. cav_un_auth*0438859 IEEE International Conference on Image Processing 2022 /29./ 20221016 20221019 Bordeaux FR JC 20000 20200 20206 2023 5 BC-A 10000 10600 10613 PO RVO:67985556 RVO:60077344 https://hdl.handle.net/11104/0336399 cav_un_auth*0309068 Západočeská univerzita v Plzni, Fakulta aplikovaných věd CZ cav_un_auth*0304556 Biologické centrum AV ČR BC CZ S 2022 85146732817 SCOPUS PUBMED 001058109503087 WOS cav_un_epca*0564843 IEEE International Conference on Image Processing 2022 : Proceedings IEEE 2022 Piscataway 3341 3345 978-1-6654-9620-9 2381-8549