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| Presentation | 2020-12-18 10:30 Pear Flower Cluster Detection Method Using Deep Learning and Branch Extraction Shunsuke Aoki, Tatsuya Yamazaki (Niigata Univ.)  PRMU2020-55 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Currently, manual pollination work in pear cultivation is a heavy burden for farmers, since a kind of pear has self-incompatibility nature. In this study, we develop a pear flower cluster detection method from camera images, which is planning to be mounted on an automated pollination system. The developed method mainly consists of a deep learning model and a branch area extraction model that uses the line enhancement filter to extract branch information. The experimental results verifies that the deep learning model detects flower clusters with 0.694 of Average Precision. Moreover, we confirm that this result can be improved by applying the branch information extracted. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | pear / flower / pollination / deep learning / Faster R-CNN / line enhancement filter / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 300, PRMU2020-55, pp. 99-104, Dec. 2020. | |
| Paper # | PRMU2020-55 | |
| Date of Issue | 2020-12-10 (PRMU) | |
| 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 | PRMU2020-55 |
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| Conference Information | |
| Committee | PRMU |
| Conference Date | 2020-12-17 - 2020-12-18 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Transfer learning and few shot learning |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2020-12-PRMU |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Pear Flower Cluster Detection Method Using Deep Learning and Branch Extraction |
| Sub Title (in English) | |
| Keyword(1) | pear |
| Keyword(2) | flower |
| Keyword(3) | pollination |
| Keyword(4) | deep learning |
| Keyword(5) | Faster R-CNN |
| Keyword(6) | line enhancement filter |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Shunsuke Aoki |
| 1st Author's Affiliation | Niigata University (Niigata Univ.) |
| 2nd Author's Name | Tatsuya Yamazaki |
| 2nd Author's Affiliation | Niigata University (Niigata Univ.) |
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| Speaker | Author-1 |
| Date Time | 2020-12-18 10:30:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2020-55 |
| Volume (vol) | vol.120 |
| Number (no) | no.300 |
| Page | pp.99-104 |
| #Pages | 6 |
| Date of Issue | 2020-12-10 (PRMU) |
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