TY - GEN
T1 - USK-COFFEE Dataset
T2 - 6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022
AU - Febriana, Alifya
AU - Muchtar, Kahlil
AU - Dawood, Rahmad
AU - Lin, Chih Yang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Coffee is one of the plantation commodities that plays a big role in the world economy. According to the classification of coffee, each type of coffee has various shapes and textures. Traditional human visual sorting of coffee beans is time-consuming, labor-intensive, and may result in low-quality coffee due to work stress and exhaustion. The contribution of this paper is twofold. First, a new dataset, called USK-Coffee, which contains a total of 8.000 images and is divided into 4 classes, is created and made publicly available. To the best of our knowledge, the USK-Coffee dataset is currently the most comprehensive green coffee bean dataset. Second, this study aims to offer a lightweight and understandable intelligent coffee bean sort accurately system that uses deep learning (DL) to assist farmers in sorting green bean arabica by variety. To be specific, this paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, MobileNetV2, and ResNet-18. These models achieved an average classification accuracy of 81.31% and 81.12%, respectively. The dataset is available at: http://comvis.unsyiah.ac.id/usk-coffee/
AB - Coffee is one of the plantation commodities that plays a big role in the world economy. According to the classification of coffee, each type of coffee has various shapes and textures. Traditional human visual sorting of coffee beans is time-consuming, labor-intensive, and may result in low-quality coffee due to work stress and exhaustion. The contribution of this paper is twofold. First, a new dataset, called USK-Coffee, which contains a total of 8.000 images and is divided into 4 classes, is created and made publicly available. To the best of our knowledge, the USK-Coffee dataset is currently the most comprehensive green coffee bean dataset. Second, this study aims to offer a lightweight and understandable intelligent coffee bean sort accurately system that uses deep learning (DL) to assist farmers in sorting green bean arabica by variety. To be specific, this paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, MobileNetV2, and ResNet-18. These models achieved an average classification accuracy of 81.31% and 81.12%, respectively. The dataset is available at: http://comvis.unsyiah.ac.id/usk-coffee/
KW - coffee beans
KW - Convolutional Neural Network (CNN)
KW - deep learning
KW - sorting coffee
UR - http://www.scopus.com/inward/record.url?scp=85138377980&partnerID=8YFLogxK
U2 - 10.1109/CyberneticsCom55287.2022.9865489
DO - 10.1109/CyberneticsCom55287.2022.9865489
M3 - 會議論文篇章
AN - SCOPUS:85138377980
T3 - Proceedings - 2022 IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022
SP - 469
EP - 473
BT - Proceedings - 2022 IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 June 2022 through 18 June 2022
ER -