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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45EA4GB
Repositorysid.inpe.br/sibgrapi/2021/09.14.22.45
Last Update2021:09.14.22.45.20 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.14.22.45.20
Metadata Last Update2022:09.10.00.16.17 (UTC) administrator
Citation KeyLucenaLisboaLimaSilv:2021:CoLeDi
TitleCoffee Leaf Diseases Identification and Severity Classification using Deep Learning
FormatOn-line
Year2021
Access Date2024, Apr. 26
Number of Files1
Size643 KiB
2. Context
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, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeUndergraduate Work
History (UTC)2021-09-14 22:45:21 :: eall@ic.ufal.br -> administrator ::
2022-09-10 00:16:17 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
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%.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2021 > Coffee Leaf Diseases...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/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
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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