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CoffeeSE: Interpretable Transfer Learning Method for Estimating the Severity of Coffee Rust

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Information Management and Big Data (SIMBig 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1577))

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Abstract

Coffee is one of the most important agricultural products and consumed beverages in the world. Then, adequate control of the diseases is necessary to guarantee its production. Coffee rust is a relevant coffee disease, which is caused by the fungus hemileia vastatrix. Recently, deep learning techniques have been used to identify coffee diseases and the severity of each disease. In this paper, we propose a new interpretable transfer learning method to estimate the severity of coffee rust called CoffeeSE. The proposed method consists of four stages: Leaf segmentation, patch sampling, patch-based classification, and quantification/interpretation analysis. On the classification stage, a Brazilian dataset is used to transfer by fine-tuning new weights to a pre-trained classifier. So, this new classifier is tested in Peruvian coffee leaves infected with coffee rust. Our approach shows acceptable quantification results according to an expert agronomist. In addition, an interpretability module of the patch-classifier is proposed to provide a visual and textual explanation of the most relevant pixels used in the classification process.

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Author information

Authors and Affiliations

  1. University of São Paulo, São Paulo, Brazil

    Filomen Incahuanaco-Quispe

  2. Universidad Nacional de San Agustín de Arequipa, Arequipa, Peru

    Edward Hinojosa-Cardenas & Denis A. Pilares-Figueroa

  3. Pontificia Universidad Católica del Perú - PUCP, Lima, Peru

    Cesar A. Beltrán-Castañón

Authors
  1. Filomen Incahuanaco-Quispe
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  2. Edward Hinojosa-Cardenas
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  3. Denis A. Pilares-Figueroa
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  4. Cesar A. Beltrán-Castañón
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Corresponding author

Correspondence to Filomen Incahuanaco-Quispe .

Editor information

Editors and Affiliations

  1. National Institutes of Health, Bethesda, MD, USA

    Juan Antonio Lossio-Ventura

  2. Visibilia, São Paulo, Brazil

    Jorge Valverde-Rebaza

  3. Universidad Peruana de Ciencias Aplicadas, Lima, Peru

    Eduardo Díaz

  4. ENSIIE and SAMOVAR, Evry, France

    Denisse Muñante

  5. The Open University, Milton Keynes, UK

    Carlos Gavidia-Calderon

  6. Federal University of São Carlos, São Carlos, Brazil

    Alan Demétrius Baria Valejo

  7. University of Engineering and Technology UTEC, Lima, Peru

    Hugo Alatrista-Salas

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Incahuanaco-Quispe, F., Hinojosa-Cardenas, E., Pilares-Figueroa, D., Beltrán-Castañón, C.A. (2022). CoffeeSE: Interpretable Transfer Learning Method for Estimating the Severity of Coffee Rust. In: Lossio-Ventura, J.A., et al. Information Management and Big Data. SIMBig 2021. Communications in Computer and Information Science, vol 1577. Springer, Cham. https://doi.org/10.1007/978-3-031-04447-2_23

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  • DOI: https://doi.org/10.1007/978-3-031-04447-2_23

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