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Paper Abstract and Keywords
Presentation 2023-09-22 13:35
Training Data Generation Method with RGB Color Space for Cucumber Classification
Hotaka Hoshino, Takuya Shindo, Takefumi Hiraguri, Nobuhiko Itoh (NIT) IA2023-27
Abstract (in Japanese) (See Japanese page) 
(in English) Cucumber farmers classify the harvested cucumbers according to specific conditions and get the classified cucumbers on the market. During the busy season, the farmers are required to classify a large number of cucumbers, but because this classifying process requires know-how, it is difficult for employers who are not trained in the classifying process to classify cucumbers instead of the cucumber farmers. Therefore, cucumber farmers must perform the classifying process themselves, which wastes tons of time. As a result, farmers have less time to spend on processes that they would like to spend on, such as cultivation for the next harvest, resulting in a decrease of profits. A system that allows anyone to easily identify the grade of cucumbers has been proposed in previous work. In this system, images of cucumbers are input to a device built with a CNN (Convolution Neural Network), and the device outputs the classification results. The improvement of estimation accuracy is required for the widely spread and promotion of the device, and a large amount of training data is required to improve estimation accuracy. Since the generation of a large amount of training data causes an increased burden on farmers, this paper proposes a method to improve estimation accuracy without increasing the number of training data. Finally, this paper shows the effectiveness of the proposed method by evaluating the estimation accuracy using classification types based on an actual agricultural cooperative union.
Keyword (in Japanese) (See Japanese page) 
(in English) Smart agriculture / IoT / Machine learning / Cucumber / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 193, IA2023-27, pp. 101-104, Sept. 2023.
Paper # IA2023-27 
Date of Issue 2023-09-14 (IA) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IA2023-27

Conference Information
Committee IA  
Conference Date 2023-09-21 - 2023-09-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univeristy 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Internet Operation and Management, Network Architecture, Communication Protocols, IoT, etc. 
Paper Information
Registration To IA 
Conference Code 2023-09-IA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Training Data Generation Method with RGB Color Space for Cucumber Classification 
Sub Title (in English)  
Keyword(1) Smart agriculture  
Keyword(2) IoT  
Keyword(3) Machine learning  
Keyword(4) Cucumber  
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1st Author's Name Hotaka Hoshino  
1st Author's Affiliation Nippon Institute of Technology (NIT)
2nd Author's Name Takuya Shindo  
2nd Author's Affiliation Nippon Institute of Technology (NIT)
3rd Author's Name Takefumi Hiraguri  
3rd Author's Affiliation Nippon Institute of Technology (NIT)
4th Author's Name Nobuhiko Itoh  
4th Author's Affiliation Nippon Institute of Technology (NIT)
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Speaker Author-1 
Date Time 2023-09-22 13:35:00 
Presentation Time 25 minutes 
Registration for IA 
Paper # IA2023-27 
Volume (vol) vol.123 
Number (no) no.193 
Page pp.101-104 
#Pages 4 
Date of Issue 2023-09-14 (IA) 

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