Coffee Beans Classification Using Convolutional Neural Networks Based On Extraction Value Analysis In Grayscale Color Space
Coffee is a vital agricultural commodity, and precise classification of coffee beans is crucial for quality assessment and agricultural practices. In this study, we propose a methodology utilizing Convolutional Neural Networks (CNN) based on ResNet-101 architecture for coffee bean classification. Th...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Batam
2025-01-01
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| Series: | Journal of Applied Informatics and Computing |
| Subjects: | |
| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8916 |
| _version_ | 1826799692464259072 |
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| author | Bagus Raffi Santoso Christy Atika Sari Eko Hari Rachmawanto |
| author_facet | Bagus Raffi Santoso Christy Atika Sari Eko Hari Rachmawanto |
| author_sort | Bagus Raffi Santoso |
| collection | DOAJ |
| description | Coffee is a vital agricultural commodity, and precise classification of coffee beans is crucial for quality assessment and agricultural practices. In this study, we propose a methodology utilizing Convolutional Neural Networks (CNN) based on ResNet-101 architecture for coffee bean classification. The novelty of our approach lies in the integration of comprehensive feature extraction from grayscale coffee bean images, including mean, standard deviation, skewness, energy, entropy, and smoothness, with the transfer learning capabilities of CNN. Through this integration, we achieved exceptional classification performance, with the CNN model attaining accuracy, recall, precision, and F1-score metrics of 99.44% and 100% on the training data, and 100% on the testing data. These results underscore the robustness and generalization capability of our methodology in accurately classifying coffee bean types. While the dataset used in this study is experimental, the comprehensive feature extraction and the effectiveness of the CNN architecture suggest the potential for accurate classification of coffee bean types beyond the experimental data, provided the new data shares similar characteristics to the collected samples. For future research, we recommend exploring the integration of two transfer learning techniques within CNN architectures to further enhance coffee bean classification systems. Specifically, leveraging pre-trained CNN models as a foundation for feature extraction, while simultaneously fine-tuning specific layers to adapt to the nuances of coffee bean classification tasks, could offer improved model performance and scalability. |
| first_indexed | 2025-03-17T00:39:07Z |
| format | Article |
| id | doaj.art-e1e5399920e0415a9e82a44c6e3d876d |
| institution | Directory Open Access Journal |
| issn | 2548-6861 |
| language | English |
| last_indexed | 2025-03-17T00:39:07Z |
| publishDate | 2025-01-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj.art-e1e5399920e0415a9e82a44c6e3d876d2025-02-21T03:39:51ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612025-01-0191313710.30871/jaic.v9i1.89166513Coffee Beans Classification Using Convolutional Neural Networks Based On Extraction Value Analysis In Grayscale Color SpaceBagus Raffi Santoso0Christy Atika Sari1Eko Hari Rachmawanto2Department of Informatics Engineering, Faculty of Computer Science, University of Dian NuswantoroDepartment of Informatics Engineering, Faculty of Computer Science, University of Dian NuswantoroDepartment of Informatics Engineering, Faculty of Computer Science, University of Dian NuswantoroCoffee is a vital agricultural commodity, and precise classification of coffee beans is crucial for quality assessment and agricultural practices. In this study, we propose a methodology utilizing Convolutional Neural Networks (CNN) based on ResNet-101 architecture for coffee bean classification. The novelty of our approach lies in the integration of comprehensive feature extraction from grayscale coffee bean images, including mean, standard deviation, skewness, energy, entropy, and smoothness, with the transfer learning capabilities of CNN. Through this integration, we achieved exceptional classification performance, with the CNN model attaining accuracy, recall, precision, and F1-score metrics of 99.44% and 100% on the training data, and 100% on the testing data. These results underscore the robustness and generalization capability of our methodology in accurately classifying coffee bean types. While the dataset used in this study is experimental, the comprehensive feature extraction and the effectiveness of the CNN architecture suggest the potential for accurate classification of coffee bean types beyond the experimental data, provided the new data shares similar characteristics to the collected samples. For future research, we recommend exploring the integration of two transfer learning techniques within CNN architectures to further enhance coffee bean classification systems. Specifically, leveraging pre-trained CNN models as a foundation for feature extraction, while simultaneously fine-tuning specific layers to adapt to the nuances of coffee bean classification tasks, could offer improved model performance and scalability.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8916coffee beansconvolutional neural networksimage classificationimage extractionresnet-101 |
| spellingShingle | Bagus Raffi Santoso Christy Atika Sari Eko Hari Rachmawanto Coffee Beans Classification Using Convolutional Neural Networks Based On Extraction Value Analysis In Grayscale Color Space Journal of Applied Informatics and Computing coffee beans convolutional neural networks image classification image extraction resnet-101 |
| title | Coffee Beans Classification Using Convolutional Neural Networks Based On Extraction Value Analysis In Grayscale Color Space |
| title_full | Coffee Beans Classification Using Convolutional Neural Networks Based On Extraction Value Analysis In Grayscale Color Space |
| title_fullStr | Coffee Beans Classification Using Convolutional Neural Networks Based On Extraction Value Analysis In Grayscale Color Space |
| title_full_unstemmed | Coffee Beans Classification Using Convolutional Neural Networks Based On Extraction Value Analysis In Grayscale Color Space |
| title_short | Coffee Beans Classification Using Convolutional Neural Networks Based On Extraction Value Analysis In Grayscale Color Space |
| title_sort | coffee beans classification using convolutional neural networks based on extraction value analysis in grayscale color space |
| topic | coffee beans convolutional neural networks image classification image extraction resnet-101 |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8916 |
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