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).