Classification of Arabica coffee bean images fromroasting using the convolutional neural network Resnet50v2 method with transfer learning

Halim, Syukur, Suhartono, Suhartono and Imamudin, Mochamad ORCID: https://orcid.org/0009-0006-7522-3710 (2025) Classification of Arabica coffee bean images fromroasting using the convolutional neural network Resnet50v2 method with transfer learning. Research Horizon, 5 (6). pp. 3347-3358. ISSN 2807-9531

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Abstract

The roasting level of Arabica coffee beans plays a crucial role in determining product quality, sensory characteristics, and market value, yet its assessment in practice is often subjective and inconsistent due to manual visual inspection. This study aims to develop a roasting level classification model for Arabica coffee beans using the ResNet50V2 Convolutional Neural Network (CNN) architecture based on transfer learning. The dataset used consists of Arabica coffee bean images with four roasting levels (Green, Light, Medium, and Dark) obtained from a publicly available dataset. Three training scenarios were applied using data split ratios of 60:40, 70:30, and 80:20, with each model trained for 10 epochs under identical experimental settings. Model performance was evaluated using accuracy and F1-score. The best results were achieved in the 80:20 scenario, with a validation accuracy of 98.75% and an F1-score of 0.9875. These results indicate that increasing the proportion of training data significantly improves model stability and classification accuracy. This study contributes by providing an objective, image-based approach for roasting level classification and demonstrating the effect of training data proportion on CNN performance to support coffee quality control.

Item Type:	Journal Article
Keywords:	classification; coffee bean image; convolutional neural network; resnet50v2; roasting; transfer learning
Subjects:	01 MATHEMATICAL SCIENCES > 0101 Pure Mathematics > 010101 Algebra and Number Theory
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Dr Suhartono M.Kom
Date Deposited:	03 Aug 2026 10:14

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