Classification on passion fruit's ripeness using K-means clustering and artificial neural network

This purpose of this study is to identify the level of ripeness of the passion fruit. The levels are classified into three distinguished stages: fruit in a ripe stage, a nearly ripe stage, and an unripe stage. The passion fruit-sorting system with artificial intelligence is an innovation of fruit sorting technology for industrial markets because it is very cost efficient and effective for a large production process instead of relying on manual labor process. The method used in this research is K-Means Clustering to perform passion fruit segmentation and Artificial Neural Network for classification based on RGB and A features. The input data is passion fruit video from 6 different sides. This study uses 75 passion fruit videos as training data and 20 videos as data testing with duration 5 seconds per video. The result achieves system accuracy of 90% with classification errors occur in the nearly ripe and unripe fruit due to the color closeness.