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

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.