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IJMLC 2022 Vol.12(4): 113-118 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2022.12.4.1089

Black Sigatoka Classification Using Convolutional Neural Networks

Cristian A. Escudero, Andrés F. Calvo, and Arley Bejarano

Abstract—In this paper we present a methodology for the automatic recognition of black Sigatoka in commercial banana crops. This method uses a LeNet convolutional neural network to detect the progress of infection by the disease in different regions of a leaf image; using this information, we trained a decision tree in order to classify the level of infection severity. The methodology was validated with an annotated database, which was built in the process of this work and which can be compared with other state-of-the-art alternatives. The results show that the method is robust against atypical values and photometric variations.

Index Terms—Black sigatoka, convolutional neural network, decision tree, plant disease detection.

All authors are with Faculty of Engineering, Tecnológica University of Pereira, Pereira, Risaralda, Colombia (e-mail: cristian-escuder@utp.edu.co, afcalvo@utp.edu.co, abjarano@utp.edu.co).


Cite: Cristian A. Escudero, Andrés F. Calvo, and Arley Bejarano, "Black Sigatoka Classification Using Convolutional Neural Networks," International Journal of Machine Learning and Computing vol. 12, no. 4, pp. 113-118, 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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