Näytä suppeat kuvailutiedot

dc.contributor.authorÄrje, Johanna
dc.contributor.authorMelvad, Claus
dc.contributor.authorJeppesen, Mads Rosenhøj
dc.contributor.authorMadsen, Sigurd Agerskov
dc.contributor.authorRaitoharju, Jenni
dc.contributor.authorRasmussen, Maria Strandgård
dc.contributor.authorIosifidis, Alexandros
dc.contributor.authorTirronen, Ville
dc.contributor.authorGabbouj, Moncef
dc.contributor.authorMeissner, Kristian
dc.contributor.authorHøye, Toke Thomas
dc.date.accessioned2020-07-28T04:21:24Z
dc.date.available2020-07-28T04:21:24Z
dc.date.issued2020
dc.identifier.citationÄrje, J., Melvad, C., Jeppesen, M. R., Madsen, S. A., Raitoharju, J., Rasmussen, M. S., Iosifidis, A., Tirronen, V., Gabbouj, M., Meissner, K., & Høye, T. T. (2020). Automatic image‐based identification and biomass estimation of invertebrates. <i>Methods in Ecology and Evolution</i>, <i>11</i>(8), 922-931. <a href="https://doi.org/10.1111/2041-210X.13428" target="_blank">https://doi.org/10.1111/2041-210X.13428</a>
dc.identifier.otherCONVID_41645207
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/71246
dc.description.abstractUnderstanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessitates more biomonitoring but the time-consuming sorting and expert-based identification of taxa pose strong limitations on how many insect samples can be processed. In turn, this affects the scale of efforts to map and monitor invertebrate diversity altogether. Given recent advances in computer vision, we propose to enhance the standard human expert-based identification approach involving manual sorting and identification with an automatic image-based technology. We describe a robot-enabled image-based identification machine, which can automate the process of invertebrate sample sorting, specimen identification and biomass estimation. We use the imaging device to generate a comprehensive image database of terrestrial arthropod species which is then used to test classification accuracy, that is, how well the species identity of a specimen can be predicted from images taken by the machine. We also test sensitivity of the classification accuracy to the camera settings (aperture and exposure time) to move forward with the best possible image quality. We use state-of-the-art Resnet-50 and InceptionV3 convolutional neural networks for the classification task. The results for the initial dataset are very promising as we achieved an average classification accuracy of 0.980. While classification accuracy is high for most species, it is lower for species represented by less than 50 specimens. We found significant positive relationships between mean area of specimens derived from images and their dry weight for three species of Diptera. The system is general and can easily be used for other groups of invertebrates as well. As such, our results pave the way for generating more data on spatial and temporal variation in invertebrate abundance, diversity and biomass.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherWiley
dc.relation.ispartofseriesMethods in Ecology and Evolution
dc.rightsCC BY 4.0
dc.subject.otherbiodiversity
dc.subject.otherclassification
dc.subject.otherconvolutional neural network
dc.subject.otherdeep learning
dc.subject.otherinsects
dc.subject.othermachine learning
dc.subject.otherspiders
dc.titleAutomatic image‐based identification and biomass estimation of invertebrates
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202007285395
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineStatisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange922-931
dc.relation.issn2041-210X
dc.relation.numberinseries8
dc.relation.volume11
dc.type.versionpublishedVersion
dc.rights.copyright© 2020 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society
dc.rights.accesslevelopenAccessfi
dc.subject.ysobiodiversiteetti
dc.subject.ysohämähäkit
dc.subject.ysokoneoppiminen
dc.subject.ysoluokitus (toiminta)
dc.subject.ysotunnistaminen
dc.subject.ysoekosysteemit (ekologia)
dc.subject.ysohyönteiset
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p5496
jyx.subject.urihttp://www.yso.fi/onto/yso/p2595
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p12668
jyx.subject.urihttp://www.yso.fi/onto/yso/p8265
jyx.subject.urihttp://www.yso.fi/onto/yso/p4997
jyx.subject.urihttp://www.yso.fi/onto/yso/p1983
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1111/2041-210X.13428
jyx.fundinginformationAarhus Universitet; Villum Fonden, Grant/Award Number: 17523
dc.type.okmA1


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