The ripening stage is a stage where the fruit is ready to be harvested. During
ripening, pectin activity is observed to trigger parenchyma cell wall middle
lamella dissolution of a fruit. Additionally, the ripening stage also affects the
changing appearance of the fruit. Thus, this research aims to develop a
classification model based on ANN that can predict or classify the ripening
stage based on either pectin activity or fruit appearance. The study will focus
specifically on Ficus carica (fig). To achieve the objective, the researchers
developed two Multilayer Perceptron (MLP) models: figNN and pectinNN.
We trained figNN using features extracted from images of figs with different
ripening stages, and pectinNN with a set of the statistical value of pectin
activity such as weight (W), brix of sugar (BS), extraction yield (EY), and
degree of esterification (ED) from 30 figs with varying degree of ripening.
From the result of this research, figNN and pectinNN can distinguish the
ripening stage based on either the chemical properties or the images.
Furthermore, we can also show that the image-based classification is more
accurate than the pectin-based classification. For future work, the study of
the correlation between pectin and image features is highly encouraged.