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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaInformation Management and Big Data - 8th Annual International Conference, SIMBig 2021, Proceedings
EditoresJuan Antonio Lossio-Ventura, Jorge Valverde-Rebaza, Eduardo Díaz, Denisse Muñante, Carlos Gavidia-Calderon, Alan Demétrius Valejo, Hugo Alatrista-Salas
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas340-355
Número de páginas16
ISBN (versión impresa)9783031044465
DOI
EstadoPublicada - 2022
Evento8th Annual International Conference on Information Management and Big Data, SIMBig 2021 - Virtual, Online
Duración: 1 dic. 20213 dic. 2021

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1577 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937

Conferencia

Conferencia8th Annual International Conference on Information Management and Big Data, SIMBig 2021
CiudadVirtual, Online
Período1/12/213/12/21

Nota bibliográfica

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

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