1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPEW34M/45EA4GB |
Repository | sid.inpe.br/sibgrapi/2021/09.14.22.45 |
Last Update | 2021:09.14.22.45.20 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2021/09.14.22.45.20 |
Metadata Last Update | 2022:09.10.00.16.17 (UTC) administrator |
Citation Key | LucenaLisboaLimaSilv:2021:CoLeDi |
Title | Coffee Leaf Diseases Identification and Severity Classification using Deep Learning |
Format | On-line |
Year | 2021 |
Access Date | 2024, May 04 |
Number of Files | 1 |
Size | 643 KiB |
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2. Context | |
Author | 1 de Lucena Lisboa, Eduardo Antônio 2 Lima do Nascimento Júnior, Givanildo 3 da Silva Queiroz, Fabiane |
Affiliation | 1 Universidade Federal de Alagoas 2 Universidade Federal de Alagoas 3 Universidade Federal de Alagoas |
Editor | Paiva, 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 Address | eall@ic.ufal.br |
Conference Name | Conference on Graphics, Patterns and Images, 34 (SIBGRAPI) |
Conference Location | Gramado, RS, Brazil (virtual) |
Date | 18-22 Oct. 2021 |
Publisher | Sociedade Brasileira de Computação |
Publisher City | Porto Alegre |
Book Title | Proceedings |
Tertiary Type | Undergraduate Work |
History (UTC) | 2021-09-14 22:45:21 :: eall@ic.ufal.br -> administrator :: 2022-09-10 00:16:17 :: administrator -> :: 2021 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Keywords | machine learning BRACOL diseases identificantion and classification |
Abstract | In 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%. |
Arrangement | urlib.net > SDLA > Fonds > SIBGRAPI 2021 > Coffee Leaf Diseases... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/45EA4GB |
zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/45EA4GB |
Language | en |
Target File | Machine_Learning_Techniques_Aimed_atthe_Identification_and_Classification_ofLeaf_Diseases_and_Pests.pdf |
User Group | eall@ic.ufal.br |
Visibility | shown |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPEW34M/45PQ3RS |
Citing Item List | sid.inpe.br/sibgrapi/2021/11.12.11.46 5 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | archivingpolicy 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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