@InProceedings{LucenaLisboaLimaSilv:2021:CoLeDi,
               author = "de Lucena Lisboa, Eduardo Ant{\^o}nio and Lima do Nascimento 
                         J{\'u}nior, Givanildo and da Silva Queiroz, Fabiane",
          affiliation = "{Universidade Federal de Alagoas} and {Universidade Federal de 
                         Alagoas} and {Universidade Federal de Alagoas}",
                title = "Coffee Leaf Diseases Identification and Severity Classification 
                         using Deep Learning",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             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%.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45EA4GB",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45EA4GB",
           targetfile = "
                         
                         Machine_Learning_Techniques_Aimed_atthe_Identification_and_Classification_ofLeaf_Diseases_and_Pests.pdf",
        urlaccessdate = "2024, May 02"
}