An AI-Based Open-Source Software for Varroa Mite Fall Analysis in Honeybee Colonies

Jesús Yániz (), Matías Casalongue, Francisco Javier Martinez- de-Pison, Miguel Angel Silvestre, Beeguards Consortium, Pilar Santolaria and Jose Divasón
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Jesús Yániz: BIOFITER Research Group, Environmental Sciences Institute (IUCA), Department of Animal Production and Food Sciences, University of Zaragoza, 22071 Huesca, Spain
Matías Casalongue: BIOFITER Research Group, Environmental Sciences Institute (IUCA), Department of Animal Production and Food Sciences, University of Zaragoza, 22071 Huesca, Spain
Francisco Javier Martinez- de-Pison: Department of Mechanical Engineering, University of La Rioja, 26004 Logroño, Spain
Miguel Angel Silvestre: Department of Cell Biology, Functional Biology and Physical Anthropology, University of Valencia, 46100 Burjassot, Spain
Beeguards Consortium: BIOFITER Research Group, Environmental Sciences Institute (IUCA), Department of Animal Production and Food Sciences, University of Zaragoza, 22071 Huesca, Spain
Pilar Santolaria: BIOFITER Research Group, Environmental Sciences Institute (IUCA), Department of Animal Production and Food Sciences, University of Zaragoza, 22071 Huesca, Spain
Jose Divasón: Department of Mathematics and Computer Science, University of La Rioja, 26006 Logroño, Spain

Agriculture, 2025, vol. 15, issue 9, 1-16

Abstract: Infestation 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 (R 2 = 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.

Keywords: honeybee; Apis mellifera; Varroa mites; Varroa diagnosis; artificial intelligence; YOLO; deep learning; smartphone (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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