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		<citationkey>LucenaLisboaLimaSilv:2021:CoLeDi</citationkey>
		<title>Coffee Leaf Diseases Identification and Severity Classification using Deep Learning</title>
		<format>On-line</format>
		<year>2021</year>
		<numberoffiles>1</numberoffiles>
		<size>643 KiB</size>
		<author>de Lucena Lisboa, Eduardo Antônio,</author>
		<author>Lima do Nascimento Júnior, Givanildo,</author>
		<author>da Silva Queiroz, Fabiane,</author>
		<affiliation>Universidade Federal de Alagoas,</affiliation>
		<affiliation>Universidade Federal de Alagoas,</affiliation>
		<affiliation>Universidade Federal de Alagoas,</affiliation>
		<editor>Paiva, Afonso,</editor>
		<editor>Menotti, David,</editor>
		<editor>Baranoski, Gladimir V. G.,</editor>
		<editor>Proença, Hugo Pedro,</editor>
		<editor>Junior, Antonio Lopes Apolinario,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Pagliosa, Paulo,</editor>
		<editor>dos Santos, Thiago Oliveira,</editor>
		<editor>e Sá, Asla Medeiros,</editor>
		<editor>da Silveira, Thiago Lopes Trugillo,</editor>
		<editor>Brazil, Emilio Vital,</editor>
		<editor>Ponti, Moacir A.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Avila, Sandra,</editor>
		<e-mailaddress>eall@ic.ufal.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)</conferencename>
		<conferencelocation>Gramado, RS, Brazil (virtual)</conferencelocation>
		<date>18-22 Oct. 2021</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Undergraduate Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>machine learning, BRACOL, diseases identificantion and classification.</keywords>
		<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%.</abstract>
		<language>en</language>
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		<usergroup>eall@ic.ufal.br</usergroup>
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