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Print: ISSN 0914-4935
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.
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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Sensors and Materials
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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
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 32, Number 11(2) (2020)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 3647-3657
S&M2365 Research Paper of Special Issue https://doi.org/10.18494/SAM.2020.3097 Published: November 18, 2020 Development of a Novel Autoinspection System for Paddy Seed Early-germination Performance [PDF] An-Qin Xu, Shi-Jie Luo, Feng-Yi Liao, I-Cheng Chen, Mao-Chien Chien, and Kuo-Yi Huang (Received July 22, 2020; Accepted October 11, 2020) Keywords: paddy seedlings, machine vision, autoinspection
We present a novel vision machine for autoinspecting paddy seed early germination. The system comprises an inlet–outlet mechanism, machine vision hardware and software, and a control system for inspecting paddy seed early germination. Differences in color are used to segment the structure of germinating seedlings. A thinning operator is employed to extract the skeleton of the root system, and the skeleton pruning method is used to remove branched roots and extract taproots. Features such as the length and width of the taproot and the length and curvature of the seed axis are provided as the input neurons of neural networks to classify seed germination as “normal” or “abnormal”. The inspection accuracy was found to be 86.09%. The experimental results indicated that early-germinating paddy seeds can be inspected efficiently by using the developed system.
Corresponding author: Kuo-Yi Huang  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article An-Qin Xu, Shi-Jie Luo, Feng-Yi Liao, I-Cheng Chen, Mao-Chien Chien, and Kuo-Yi Huang, Development of a Novel Autoinspection System for Paddy Seed Early-germination Performance, Sens. Mater., Vol. 32, No. 11, 2020, p. 3647-3657. |
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