Essential Oil Plants Image Classification Using Xception Model
Jeremy Onesimus Carnagie, Aditya Rio Prabowo, Eko Prasetya Budiana, Ivan Kristianto Singgih
Abstract
Machine learning-based technology has now been widely applied in various fields. In recent times, image classification is a machine learning-based technology that has very diverse uses, since its effectiveness for performing classification of images can really be helpful for many kinds of work such as sorting or detecting. In this work, we have created a customized Convolutional Neural Network (CNN) using Transfer Learning methods of the Xception Model to classify images of essential oil plants. Our Convolutional Neural Network is able to identify and classify images of essential oil plants with a good degree of accuracy, which will be concluded in a confusion matrix table. The same usage concept of CNN can also be generalized to perform other plants image classifications tasks.