Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3PJ8FTL |
Repository | sid.inpe.br/sibgrapi/2017/09.05.12.21 |
Last Update | 2017:09.05.12.21.34 elison.a.lins@gmail.com |
Metadata | sid.inpe.br/sibgrapi/2017/09.05.12.21.35 |
Metadata Last Update | 2020:02.20.22.06.47 administrator |
Citation Key | LinsRied:2017:MeCoCl |
Title | Uma metodologia de contagem e classificação de afídeos utilizando visão computacional  |
Format | On-line |
Year | 2017 |
Access Date | 2021, Jan. 25 |
Number of Files | 1 |
Size | 2110 KiB |
Context area | |
Author | 1 Lins, Elison Alfeu 2 Rieder, Rafael |
Affiliation | 1 Universidade de Passo Fundo 2 Universidade de Passo Fundo |
Editor | Torchelsen, 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 Address | elison.a.lins@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ |
Date | Oct. 17-20, 2017 |
Book Title | Proceedings |
Publisher | Sociedade Brasileira de Computação |
Publisher City | Porto Alegre |
Tertiary Type | Work in Progress |
History | 2017-09-05 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 Stage | completed |
Transferable | 1 |
Keywords | OpenCv, tensorflow, aphids, counting, classification. |
Abstract | AbstractAphids 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 Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPAW/3PJ8FTL |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PJ8FTL |
Language | pt |
Target File | 2017_sibgrapi_WIP_Elison.pdf |
User Group | elison.a.lins@gmail.com |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PJT9LS 8JMKD3MGPAW/3PKCC58 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber 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 |
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