000160785 001__ 160785
000160785 005__ 20251017144552.0
000160785 0247_ $$2doi$$a10.3390/agriculture15090969
000160785 0248_ $$2sideral$$a143977
000160785 037__ $$aART-2025-143977
000160785 041__ $$aeng
000160785 100__ $$0(orcid)0000-0001-5316-1703$$aYániz, Jesús$$uUniversidad de Zaragoza
000160785 245__ $$aAn AI-Based Open-Source Software for Varroa Mite Fall Analysis in Honeybee Colonies
000160785 260__ $$c2025
000160785 5060_ $$aAccess copy available to the general public$$fUnrestricted
000160785 5203_ $$aInfestation by Varroa destructor is responsible for high mortality rates in Apis mellifera colonies worldwide. This study was designed to develop and test under field conditions a new free software (VarroDetector) based on a deep learning approach for the automated detection and counting of Varroa mites using smartphone images of sticky boards collected in honeybee colonies. A total of 204 sheets were collected, divided into four frames using green strings, and photographed under controlled lighting conditions with different smartphone models at a minimum resolution of 48 megapixels. The Varroa detection algorithm comprises two main steps: First, the region of interest where Varroa mites must be counted is established. From there, a one-stage detector is used, namely YOLO v11 Nano. A final verification was conducted counting the number of Varroa mites present on new sticky sheets both manually through visual inspection and using the VarroDetector software and comparing these measurements with the actual number of mites present on the sheet (control). The results obtained with the VarroDetector software were highly correlated with the control (R2 = 0.98 to 0.99, depending on the smartphone camera used), even when using a smartphone for which the software was not previously trained. When Varroa mite numbers were higher than 50 per sheet, the results of VarroDetector were more reliable than those obtained with visual inspection performed by trained operators, while the processing time was significantly reduced. It is concluded that the VarroDetector software Version 1.0 (v. 1.0) is a reliable and efficient tool for the automated detection and counting of Varroa mites present on sticky boards collected in honeybee colonies.
000160785 536__ $$9info:eu-repo/grantAgreement/ES/MCIU/PID2023-148475OB-I00
000160785 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000160785 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000160785 700__ $$aCasalongue, Matías$$uUniversidad de Zaragoza
000160785 700__ $$aMartinez-de-Pison, Francisco Javier
000160785 700__ $$aSilvestre, Miguel Angel
000160785 700__ $$aConsortium, Beeguards
000160785 700__ $$0(orcid)0000-0001-8991-325X$$aSantolaria, Pilar$$uUniversidad de Zaragoza
000160785 700__ $$aDivasón, Jose
000160785 7102_ $$12008$$2700$$aUniversidad de Zaragoza$$bDpto. Produc.Animal Cienc.Ali.$$cÁrea Producción Animal
000160785 773__ $$g15, 9 (2025), 969 [16 pp.]$$pAgriculture (Basel)$$tAgriculture (Basel)$$x2077-0472
000160785 8564_ $$s7447884$$uhttps://zaguan.unizar.es/record/160785/files/texto_completo.pdf$$yVersión publicada
000160785 8564_ $$s2602627$$uhttps://zaguan.unizar.es/record/160785/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000160785 909CO $$ooai:zaguan.unizar.es:160785$$particulos$$pdriver
000160785 951__ $$a2025-10-17-14:12:14
000160785 980__ $$aARTICLE