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Paper Abstract and Keywords
Presentation 2024-01-26 13:15
Estimating the best time to collect pear pollen using deep learning
Keita Endo (NIT), Tomotaka Kimura (Doshisha Univ.), Hiroyuki Shimizu (NIT), Tomohito Shimada (SATRC), Akane Shibasaki (SAFPC), Ryota Fujinuma (DKK), Yoshihiro Takemura (Tottori Univ.), Takefumi Hiraguri (NIT) CQ2023-65
Abstract (in Japanese) (See Japanese page) 
(in English) Pear pollination is generally done by artificial pollination, and pollen collection is necessary for artificial pollination. Pollen collection is hard work, requiring long hours at high altitudes, and in recent years, much of the pollen is imported. However, if an important disease occurs in a pollen exporting country, imports are stopped, resulting in a decrease in production. To solve this problem, it is necessary to mechanize the pollen collection process and strengthen the domestic supply and demand system. In this study, we propose an AI (Artificial Intelligence)-based method for estimating pear pollen quantity. Specifically, we use YOLO (You Only Look Once), a deep learning-based object detection algorithm, to detect pear blossoms on photographed branches by classifying them into five stages, from bud to blossom. The amount of pollen per branch is calculated from the number of flowers in each flowering stage detected and the average amount of pollen per flower. In this paper, we report on the evaluation of the estimation accuracy of AI-based pear pollen quantity estimation developed by assessing the classification accuracy and detection precision during the detection of blooming stages using YOLO.
Keyword (in Japanese) (See Japanese page) 
(in English) Smart agriculture / Machine learning / YOLO / Pear / Estimation of pollen amount / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 368, CQ2023-65, pp. 68-75, Jan. 2024.
Paper # CQ2023-65 
Date of Issue 2024-01-18 (CQ) 
ISSN Online edition: ISSN 2432-6380
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)
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Conference Information
Committee CQ CBE  
Conference Date 2024-01-25 - 2024-01-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Kurokawa-Onsen 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network Science, Computational Social Science, Media Quality, Communication Behaviour, etc. 
Paper Information
Registration To CQ 
Conference Code 2024-01-CQ-CBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Estimating the best time to collect pear pollen using deep learning 
Sub Title (in English)  
Keyword(1) Smart agriculture  
Keyword(2) Machine learning  
Keyword(3) YOLO  
Keyword(4) Pear  
Keyword(5) Estimation of pollen amount  
1st Author's Name Keita Endo  
1st Author's Affiliation Nippon Institute of Technology (NIT)
2nd Author's Name Tomotaka Kimura  
2nd Author's Affiliation Doshisha University (Doshisha Univ.)
3rd Author's Name Hiroyuki Shimizu  
3rd Author's Affiliation Nippon Institute of Technology (NIT)
4th Author's Name Tomohito Shimada  
4th Author's Affiliation Saitama Agricultural Technology Research Center (SATRC)
5th Author's Name Akane Shibasaki  
5th Author's Affiliation Saitama Agriculture and Forestry Promotion Center (SAFPC)
6th Author's Name Ryota Fujinuma  
6th Author's Affiliation DKK Co., Ltd. (DKK)
7th Author's Name Yoshihiro Takemura  
7th Author's Affiliation Tottori University (Tottori Univ.)
8th Author's Name Takefumi Hiraguri  
8th Author's Affiliation Nippon Institute of Technology (NIT)
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Speaker Author-1 
Date Time 2024-01-26 13:15:00 
Presentation Time 25 minutes 
Registration for CQ 
Paper # CQ2023-65 
Volume (vol) vol.123 
Number (no) no.368 
Page pp.68-75 
Date of Issue 2024-01-18 (CQ) 

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