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Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
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MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 29, Number 11 (2017)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 1599-1605
S&M1453 Research Paper of Special Issue https://doi.org/10.18494/SAM.2017.1708 Published: November 24, 2017 Development of Novel Autoclassifying System Based on Machine Vision [PDF] Kuo-Yi Huang and Ya-Ting Tu (Received April 28, 2017; Accepted August 3, 2017) Keywords: peaberry, flat bean, classification
In this paper, we present a novel machine-vision-based autoclassifying system for peaberry (PB) and flat beans (FB) of coffee. The system comprises an inlet–outlet mechanism, machinevision hardware and software, and a control system for classifying coffee. The proposed method can estimate the shape features of coffee beans, provided as input neurons of neural networks, and accordingly classify coffee beans as PB and FB. Experiments yielded classification accuracy levels of 96.97 and 95.22% for PB and FB, respectively, indicating that PB and FB can be classified efficiently using the proposed system.
Corresponding author: Kuo-Yi Huang Cite this article Kuo-Yi Huang and Ya-Ting Tu, Development of Novel Autoclassifying System Based on Machine Vision, Sens. Mater., Vol. 29, No. 11, 2017, p. 1599-1605. |
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