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IJMLC 2020 Vol.10(4): 534-541 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.4.969

Vision-Based Lettuce Growth Stage Decision Support System Using Artificial Neural Networks

Pocholo James M. Loresco and Elmer Dadios

Abstract—Machine vision approaches for lettuce growth stage prediction are continuously being developed. Previous works suggest further extensive study of computer vision features in determining plant growth. This paper presented an ANN-based decision support system of classifying lettuce growth stage by using extracted vision features that included two morphological features (area, perimeter), 12 color features (RGB, HSV, YCbCr, Lab), and five textural features (contrast, energy, correlation, entropy, and homogeneity). Image processing techniques were used to extract the required vision features, and the neural network was trained using scaled conjugate gradient back propagation. The decision support system exhibited promising results in lettuce growth stage classification.

Index Terms—Artificial neural networks, decision support, lettuce growth stage, vision features.

P. J. M. Loresco is with Electrical and Electronics Engineering Department, Far Eastern University, Philippines (e-mail: pocholo_loresco@dlsu.edu.ph, pmloresco@feutech.edu.ph).
E. P. Dadios is with the Manufacturing Engineering and Management Department, De La Salle University, Manila, Philippines (e-mail: elmer.dadios@dlsu.edu.ph).


Cite: Pocholo James M. Loresco and Elmer Dadios, "Vision-Based Lettuce Growth Stage Decision Support System Using Artificial Neural Networks," International Journal of Machine Learning and Computing vol. 10, no. 4, pp. 534-541, 2020.

Copyright © 2020 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

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • 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: ijmlc@ejournal.net

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