Close
Metadata

Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3PJ8FTL
Repositorysid.inpe.br/sibgrapi/2017/09.05.12.21
Last Update2017:09.05.12.21.34 elison.a.lins@gmail.com
Metadatasid.inpe.br/sibgrapi/2017/09.05.12.21.35
Metadata Last Update2020:02.20.22.06.47 administrator
Citation KeyLinsRied:2017:MeCoCl
TitleUma metodologia de contagem e classificação de afídeos utilizando visão computacional
FormatOn-line
Year2017
Access Date2021, Jan. 25
Number of Files1
Size2110 KiB
Context area
Author1 Lins, Elison Alfeu
2 Rieder, Rafael
Affiliation1 Universidade de Passo Fundo
2 Universidade de Passo Fundo
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addresselison.a.lins@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
DateOct. 17-20, 2017
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeWork in Progress
History2017-09-05 12:21:35 :: elison.a.lins@gmail.com -> administrator ::
2020-02-20 22:06:47 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsOpenCv, tensorflow, aphids, counting, classification.
AbstractAbstractAphids are insects that attack crops and cause damage directly, consuming the sap of plants, and indirectly, transmiting diseases. The counting and classification of these insects are fundamental for measuring and predicting crop hazards and serving as the basis for the application or not of chemicals. Traditionally, the counting process is manual, and depends of microscopes and good eyesight of the specialist, in a time-consuming task susceptible to errors. With this in mind, this paper presents a methodology and a software to automate the counting and classification of aphids using image processing, computer vision and deep learning methods. As preliminary results in a pilot study, we obtained 95.50 % correlation for the count of 28 samples containing Rhopalosiphum padi, in shortest time compared the manual method.
source Directory Contentthere are no files
agreement Directory Content
agreement.html 05/09/2017 09:21 1.2 KiB 
Conditions of access and use area
data URLhttp://urlib.net/rep/8JMKD3MGPAW/3PJ8FTL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PJ8FTL
Languagept
Target File2017_sibgrapi_WIP_Elison.pdf
User Groupelison.a.lins@gmail.com
Visibilityshown
Update Permissionnot transferred
Allied materials area
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PJT9LS
8JMKD3MGPAW/3PKCC58
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Notes area
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume

Close