Classification of Banana Types Based on The Geometrical Attributes using Artificial Neural Network Method

Abstract

Banana (Musa paradisiaca) is one of the important horticultural commodities. This study aims to measure the physical and geometrical parameters of three different bananas (Muli, Ambon, and Kepok) and to develop prediction equations using an Artificial Neural Network (ANN) model. In this study the backpropagation ANN model with supervised learning method was used. The ANN model had one output node, two hidden layers, and network architecture of 8 inputs, namely fruit weight and volume, projected area and roundness of the fruit, cross section, peel color, and geometric mean fruit cross section diameter. The data for building the model and testing the model were respectively 70% and 30% of the 150 data number in total. The results showed that the best ANN model structure for estimating Muli, Ambon and Kepok bananas was purelin-logsig-logsig with an RMSE value of 0.0077 and an R2 of 0.9999. This shows that the ANN model is highly robust to predict the banana types. Using the built model, the accuracy of the prediction results is 100%. Keywords: Artificial Neural Network, Banana fruits, Geometry attribute.