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All Journal International Journal of Electrical and Computer Engineering
Stephen, Shalwin Ambalamoottil
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Computer Science & IT Electrical & Electronics Engineering

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Coffee bean graded based on deep net models Balakrishnan Jayakumari, Bipin Nair; Koovamoola Mambilamthoda, Abrav Nanda; Stephen, Shalwin Ambalamoottil; Venkitesan, Pranav; Raghavendra, Venkatesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3084-3093

Abstract

Coffee is a widely consumed beverage, and sorting coffee beans is a critical process that ensures high-quality graded coffee products. Coffee beans were graded into nine grades in robusta types. To automate the grading process, a deep learning-based approach was developed using a large dataset of high-resolution images and data augmentation techniques. In contrast to previous studies focusing on robusta type graded into six coffee bean grads, our research extends this framework by employing robusta type into nine grades with an outperformed accuracy. The proposed work uses four deep learning models, namely residual network 34(Resnet34), inception version 3 (Inception v3), efficient network bayesian optimization (EfficientNet-B0), and visual geometry group-16(VGG-16), where trained and evaluated for coffee bean classification into nine grades. The EfficientNet-B0 model exhibited outperformed accuracy, achieving 100% in distinguishing good and bad coffee beans, even in challenging lighting and background conditions.
Co-Authors Balakrishnan Jayakumari, Bipin Nair Koovamoola Mambilamthoda, Abrav Nanda Raghavendra, Venkatesh Venkitesan, Pranav