Raw coffee bean classification for roasting suitability assessment using transfer learning

Pratondo, Agus and Zani, Tafta and Novianty, Astri and Pudjoatmodjo, Bambang (2023) Raw coffee bean classification for roasting suitability assessment using transfer learning. In: 11th IEEE Conference on Systems, Process and Control, ICSPC 2023, 16 December 2023, Malacca.

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Abstract

The classification of raw robusta coffee beans is a pivotal process with profound implications for the coffee industry. Recognizing the critical significance of this classification, this research endeavors to establish an effective and automated method for discerning coffee bean quality. The primary objective of this study is to employ advanced machine learning techniques to classify raw robusta coffee beans accurately. Specifically, we utilize deep learning models, VGG-16 and Inception V3, for this purpose. These models are trained to classify coffee beans based on their quality attributes, offering a systematic approach to segregating beans into high and low-quality categories. The research methodology encompasses comprehensive data preprocessing, model construction, and rigorous experimentation. Notably, the Inception V3 model, initialized with pre-trained weights from ImageNet, emerged as the top performer, achieving an outstanding accuracy rate of 99%. The implications of this research extend to various stakeholders within the coffee industry. By enhancing the quality assessment of raw robusta coffee beans, this research contributes to improved quality control processes. It ensures that only the finest beans proceed through the production chain, bolstering economic viability and market competitiveness for coffee-producing regions.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Classification, Coffee bean, Inception v3, Robusta
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Anis Suraya Nordin
Date Deposited:	17 Oct 2024 16:31
Last Modified:	17 Oct 2024 16:31
URI:	http://eprints.utem.edu.my/id/eprint/28118
Statistic Details:	View Download Statistic

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