An Implementation of Convolutional Neural Network for Coffee Beans Quality Classification in a Mobile Information System
Due to its massive trading in world markets, maintaining the quality of coffee is vital for the exporting countries. One approach for quality control is to have a system that can classify coffee beans based on the quality. This system can assist the small-medium coffee enterprises to monitor and secure their procurement. However, the coffee beans quality classification technology is currently unavailable to the small-medium coffee enterprises community. To address this issue, we developed a mobile application powered by a deep-learning-based model to automatically classify coffee beans quality via a mobile phone camera. The deep learning model used is chosen between ResNet-152 and VGG16 based on their performance to classify coffee beans quality. The result shows that ResNet-152 could achieve the highest accuracy of 73.3% and could also be embedded in a functional mobile application.
Robby Janandi and Tjeng Wawan Cenggoro
