136072 20260217205522.0 doi 10.3390/s24123828 sideral 139044 ART-2024-139044 eng Divasón, Jose Analysis of Varroa Mite Colony Infestation Level Using New Open Software Based on Deep Learning Techniques 2024 Access copy available to the general public Unrestricted Varroa mites, scientifically identified as Varroa destructor, pose a significant threat to beekeeping and cause one of the most destructive diseases affecting honey bee populations. These parasites attach to bees, feeding on their fat tissue, weakening their immune systems, reducing their lifespans, and even causing colony collapse. They also feed during the pre-imaginal stages of the honey bee in brood cells. Given the critical role of honey bees in pollination and the global food supply, controlling Varroa mites is imperative. One of the most common methods used to evaluate the level of Varroa mite infestation in a bee colony is to count all the mites that fall onto sticky boards placed at the bottom of a colony. However, this is usually a manual process that takes a considerable amount of time. This work proposes a deep learning approach for locating and counting Varroa mites using images of the sticky boards taken by smartphone cameras. To this end, a new realistic dataset has been built: it includes images containing numerous artifacts and blurred parts, which makes the task challenging. After testing various architectures (mainly based on two-stage detectors with feature pyramid networks), combination of hyperparameters and some image enhancement techniques, we have obtained a system that achieves a mean average precision (mAP) metric of 0.9073 on the validation set. info:eu-repo/grantAgreement/ES/AEI/PID2020-112673RB-I00 info:eu-repo/grantAgreement/ES/AEI/PID2020-116641GB-I00 info:eu-repo/grantAgreement/ES/DGA-FSE/A07_23R info:eu-repo/grantAgreement/ES/MCIU/PID2019-106570RB-I00-AEI-10.13039-501100011033 info:eu-repo/grantAgreement/ES/AEI/PID2021-123219OB-I00 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 3.5 2024 CHEMISTRY, ANALYTICAL 38 / 111 = 0.342 2024 Q2 T2 INSTRUMENTS & INSTRUMENTATION 24 / 79 = 0.304 2024 Q2 T1 ENGINEERING, ELECTRICAL & ELECTRONIC 134 / 368 = 0.364 2024 Q2 T2 0.764 2024 Instrumentation 2024 Q1 Analytical Chemistry 2024 Q1 Medicine (miscellaneous) 2024 Q2 Information Systems 2024 Q2 Biochemistry 2024 Q2 Atomic and Molecular Physics, and Optics 2024 Q2 Electrical and Electronic Engineering 2024 Q2 8.2 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Romero, Ana Martinez-de-Pison, Francisco Javier Casalongue, Matías Universidad de Zaragoza Silvestre, Miguel A. Santolaria, Pilar Universidad de Zaragoza (orcid)0000-0001-8991-325X Yániz, Jesús L. Universidad de Zaragoza (orcid)0000-0001-5316-1703 2008 700 Universidad de Zaragoza Dpto. Produc.Animal Cienc.Ali. Área Producción Animal 24, 12 (2024), 3828 Sensors Sensors 1424-8220 8022875 http://zaguan.unizar.es/record/136072/files/texto_completo.pdf Versión publicada 2727544 http://zaguan.unizar.es/record/136072/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:136072 articulos driver 2026-02-17-20:29:02 ARTICLE