Coffee Leaf Diseases Identification and Severity Classification using Deep Learning

  • Eduardo Lisboa UFAL
  • Givanildo Lima UFAL
  • Fabiane Queiroz UFAL


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%.


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LISBOA, Eduardo; LIMA, Givanildo; QUEIROZ, Fabiane. Coffee Leaf Diseases Identification and Severity Classification using Deep Learning. In: WORKSHOP DE TRABALHOS DA GRADUAÇÃO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 34. , 2021, Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 201-205. DOI: