000136072 001__ 136072
000136072 005__ 20240711103551.0
000136072 0247_ $$2doi$$a10.3390/s24123828
000136072 0248_ $$2sideral$$a139044
000136072 037__ $$aART-2024-139044
000136072 041__ $$aeng
000136072 100__ $$aDivasón, Jose
000136072 245__ $$aAnalysis of Varroa Mite Colony Infestation Level Using New Open Software Based on Deep Learning Techniques
000136072 260__ $$c2024
000136072 5060_ $$aAccess copy available to the general public$$fUnrestricted
000136072 5203_ $$aVarroa 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.
000136072 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-112673RB-I00$$9info:eu-repo/grantAgreement/ES/AEI/PID2020-116641GB-I00$$9info:eu-repo/grantAgreement/ES/DGA-FSE/A07_23R$$9info:eu-repo/grantAgreement/ES/MCIU/PID2019-106570RB-I00-AEI-10.13039-501100011033$$9info:eu-repo/grantAgreement/ES/AEI/PID2021-123219OB-I00
000136072 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000136072 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000136072 700__ $$aRomero, Ana
000136072 700__ $$aMartinez-de-Pison, Francisco Javier
000136072 700__ $$aCasalongue, Matías$$uUniversidad de Zaragoza
000136072 700__ $$aSilvestre, Miguel A.
000136072 700__ $$0(orcid)0000-0001-8991-325X$$aSantolaria, Pilar$$uUniversidad de Zaragoza
000136072 700__ $$0(orcid)0000-0001-5316-1703$$aYániz, Jesús L.$$uUniversidad de Zaragoza
000136072 7102_ $$12008$$2700$$aUniversidad de Zaragoza$$bDpto. Produc.Animal Cienc.Ali.$$cÁrea Producción Animal
000136072 773__ $$g24, 12 (2024), 3828$$pSensors$$tSensors$$x1424-8220
000136072 8564_ $$s8022875$$uhttps://zaguan.unizar.es/record/136072/files/texto_completo.pdf$$yVersión publicada
000136072 8564_ $$s2727544$$uhttps://zaguan.unizar.es/record/136072/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000136072 909CO $$ooai:zaguan.unizar.es:136072$$particulos$$pdriver
000136072 951__ $$a2024-07-11-08:37:41
000136072 980__ $$aARTICLE