Close
Metadata

Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/45EA4GB
Repositorysid.inpe.br/sibgrapi/2021/09.14.22.45
Last Update2021:09.14.22.45.20 (UTC) eall@ic.ufal.br
Metadatasid.inpe.br/sibgrapi/2021/09.14.22.45.20
Metadata Last Update2021:09.14.22.45.21 (UTC) eall@ic.ufal.br
Citation KeyLucenaLisboaLimaSilv:2021:CoLeDi
TitleCoffee Leaf Diseases Identification and Severity Classification using Deep Learning
FormatOn-line
Year2021
Access Date2021, Sep. 24
Number of Files1
Size643 KiB
Context area
Author1 de Lucena Lisboa, Eduardo Antônio
2 Lima do Nascimento Júnior, Givanildo
3 da Silva Queiroz, Fabiane
Affiliation1 Universidade Federal de Alagoas
2 Universidade Federal de Alagoas
3 Universidade Federal de Alagoas
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addresseall@ic.ufal.br
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado (Virtual), Brazil
DateOctober 18th to October 22nd, 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeUndergraduate Work
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsmachine learning
BRACOL
diseases identificantion and classification
AbstractIn this paper, we propose a method for automatic identification and classification of leaf diseases and pests in the Brazilian Arabica Coffee leaves. We developed a Machine Learning model, trained with the BRACOL public image dataset, to evaluate if a given image of a leaf has a disease or pest - Miner, Phoma, Cercospora and Rust - or if it is healthy. We then compared our model with other famous and well-known classification models, and we were able to achieve an accuracy of 98,04%, which greatly exceeds the accuracy of the other methods implemented. In addition, we developed an assessment to perform a classification related to the percentage of each leaf that is affected by the disease, achieving an accuracy of approximately 90%.
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 14/09/2021 19:45 1.3 KiB 
Conditions of access and use area
data URLhttp://urlib.net/rep/8JMKD3MGPEW34M/45EA4GB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45EA4GB
Languageen
Target FileMachine_Learning_Techniques_Aimed_atthe_Identification_and_Classification_ofLeaf_Diseases_and_Pests.pdf
User Groupeall@ic.ufal.br
Visibilityshown
Allied materials area
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
Notes area
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit 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
Description control area
e-Mail (login)eall@ic.ufal.br
update 

Close