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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Munandar, Aris
Research Center for Smart Mechatronic, National Research and Innovation, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Implementation of Image Processing and CNN for Roasted-Coffee Level Classification Anto, Irfan Asfy Fakhry; Wibowo, Jony Winaryo; Salim, Taufik Ibnu; Munandar, Aris
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5531

Abstract

The roasting process of coffee beans plays a crucial role in the development of chemicals responsible for the rich color and complex flavors characteristic of well-roasted coffee. One approach to understanding this process involves assessing the roast level, which varies in color from light to dark, with intermediate levels in between. In this study, image processing was performed using Convolutional Neural Networks (CNNs), a widely used method for image classification. The objective was to utilize the LAB color model and the CNN framework to classify the roast levels of coffee beans based on images from files or video streams. The study also details the hardware and software tools employed. A user-friendly graphical interface was developed to ensure ease of use, requiring minimal training for efficient operation. The research successfully designed, developed, and implemented an application for classifying coffee bean roast levels using two methods: LAB color model image processing and the CNN model. Consequently, the system can recognize roast levels based on the outputs from both the LAB model and the CNN model. This research represents a preliminary effort and requires further development to support more extensive studies. Ultimately, it serves as a foundation for future exploration and the application of embedded system-based solutions for assessing coffee bean maturity levels in alignment with Agtron classification standards.
Co-Authors Anto, Irfan Asfy Fakhry Salim, Taufik Ibnu Wibowo, Jony Winaryo