Apple Ripeness Estimation using Artificial Neural Network

Fruit ripeness estimation is an important process that affects its quality and subsequently its marketing. Automatic ripeness evaluation through computer vision system has been an innovative topic interesting many researchers as it provides efficient solution to the slow speed, time consumption and high cost associated with the manual assessment. In this paper, Artificial Neural Network (ANN) classification approach has been investigated to estimate the ripeness of apple fruits based on color. Several points have been dealt with in this study, namely the color features vectors, the learning pedagogy and the structure of the ANN classifier in order to obtain the best performance. Dataset used for simulation has been collected and exploited for the training and testing phases: 80 % of the total images were used for training and 20% of the total images were used for testing the classifier. Training dataset is composed by three classes representing the three different stages of apple ripeness. Simulation results showed the performance achieved by the ripeness classification system.