Palm oil classification using deep learning

Abstract

Deep Convolutional Neural Networks (CNNs) have been established as a dominant class of models for image classification problems. This study aims to apply and analyses the accuracy of deep learning for classifying ripes on palm oil fruit. The CNN used to classify 628 images into 2 different classes. Furthermore, the experiment of CNN with 5 epochs gives promising classification results with an accuracy of 98%, which is better than previous methods. To sum up, this study was successfully solving an image classification by detected and differentiated the ripeness of oil palm fruit.