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

Filomen Incahuanaco-Quispe, Edward Hinojosa-Cardenas, Denis A A. Pilares-Figueroa, Cesar A. Beltrán-Castañón

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationInformation Management and Big Data - 8th Annual International Conference, SIMBig 2021, Proceedings
EditorsJuan Antonio Lossio-Ventura, Jorge Valverde-Rebaza, Eduardo Díaz, Denisse Muñante, Carlos Gavidia-Calderon, Alan Demétrius Valejo, Hugo Alatrista-Salas
PublisherSpringer Science and Business Media Deutschland GmbH
Pages340-355
Number of pages16
ISBN (Print)9783031044465
DOIs
StatePublished - 2022
Event8th Annual International Conference on Information Management and Big Data, SIMBig 2021 - Virtual, Online
Duration: 1 Dec 20213 Dec 2021

Publication series

NameCommunications in Computer and Information Science
Volume1577 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference8th Annual International Conference on Information Management and Big Data, SIMBig 2021
CityVirtual, Online
Period1/12/213/12/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Coffee rust
  • Interpretability
  • Sampling
  • Transfer learning

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