%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.14.22.45
%2 sid.inpe.br/sibgrapi/2021/09.14.22.45.20
%T Coffee Leaf Diseases Identification and Severity Classification using Deep Learning
%D 2021
%A de Lucena Lisboa, Eduardo Antônio,
%A Lima do Nascimento Júnior, Givanildo,
%A da Silva Queiroz, Fabiane,
%@affiliation Universidade Federal de Alagoas,
%@affiliation Universidade Federal de Alagoas,
%@affiliation Universidade Federal de Alagoas,
%E Paiva, Afonso,
%E Menotti, David,
%E Baranoski, Gladimir V. G.,
%E Proença, Hugo Pedro,
%E Junior, Antonio Lopes Apolinario,
%E Papa, João Paulo,
%E Pagliosa, Paulo,
%E dos Santos, Thiago Oliveira,
%E e Sá, Asla Medeiros,
%E da Silveira, Thiago Lopes Trugillo,
%E Brazil, Emilio Vital,
%E Ponti, Moacir A.,
%E Fernandes, Leandro A. F.,
%E Avila, Sandra,
%B Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)
%C Gramado, RS, Brazil (virtual)
%8 18-22 Oct. 2021
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K machine learning, BRACOL, diseases identificantion and classification.
%X 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%.
%@language en
%3 Machine_Learning_Techniques_Aimed_atthe_Identification_and_Classification_ofLeaf_Diseases_and_Pests.pdf