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| Presentation | 2021-11-26 15:00 [Poster Presentation] Fish Catch Forecasting by Species using Smart Buoy Sensing Data Cong Cao, utsunomiya eiji, yoshihara kiyohito (KDDI Research)  SRW2021-46 SeMI2021-45 CNR2021-20 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Fish catch forecasting based on IoT(Internet of Things) data is an important theme for smart fishery, which is expected to solve the problems like know-how humanize and insufficient workers. In this paper, we build regression machine learning models (ResNet Regressor and Random Forest Regressor) based on sensor data and weather data to forecast fish catch weight from a local fishing ground in Japan. From the analysis of the relevance between features and weight target, we find that when compared with the total weight, weight target for each fish spices is more related to environment features like water temperature and atmospheric temperature. Otherwise, lack of enough data to train the model is also a big problem because the harsh environments often cause IoT system sensing failures. To solve the problem, 2 methods are discussed in this paper. The first method, train machine learning models to predict sensor values based on weather data, and use the prediction as missing data imputation. The second method, use data from several different fishing grounds. We conduct experiments to evaluated the above 2 methods for total weight and weights for some fish species. As a result, method 1 is effective for all fish species, especially effective for [Jack mackerel], which reduce the RMSE of ResNet Regressor form 22.12 to 18.26. Method 2 is effective for [Jack mackerel] [Silver-stripe round herring] and [Unicorn leatherjacket], also especially effective for [Jack mackerel], which reduce the RMSE of Random Forest Regressor form 29.27 to 26.09. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Fish catch forecasting / Weight target for each fish spices / Machine learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 266, SeMI2021-45, pp. 52-54, Nov. 2021. | |
| Paper # | SeMI2021-45 | |
| Date of Issue | 2021-11-18 (SRW, SeMI, CNR) | |
| 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 | SRW2021-46 SeMI2021-45 CNR2021-20 |
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| Conference Information | |
| Committee | SRW SeMI CNR |
| Conference Date | 2021-11-25 - 2021-11-26 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kikai-Shinko-Kaikan Bldg. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | IoT Workshop |
| Paper Information | |
| Registration To | SeMI |
| Conference Code | 2021-11-SRW-SeMI-CNR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Fish Catch Forecasting by Species using Smart Buoy Sensing Data |
| Sub Title (in English) | |
| Keyword(1) | Fish catch forecasting |
| Keyword(2) | Weight target for each fish spices |
| Keyword(3) | Machine learning |
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| 1st Author's Name | Cong Cao |
| 1st Author's Affiliation | KDDI Research, Inc (KDDI Research) |
| 2nd Author's Name | utsunomiya eiji |
| 2nd Author's Affiliation | KDDI Research, Inc (KDDI Research) |
| 3rd Author's Name | yoshihara kiyohito |
| 3rd Author's Affiliation | KDDI Research, Inc (KDDI Research) |
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| Speaker | Author-1 |
| Date Time | 2021-11-26 15:00:00 |
| Presentation Time | 120 minutes |
| Registration for | SeMI |
| Paper # | SRW2021-46, SeMI2021-45, CNR2021-20 |
| Volume (vol) | vol.121 |
| Number (no) | no.265(SRW), no.266(SeMI), no.267(CNR) |
| Page | pp.65-67(SRW), pp.52-54(SeMI), pp.42-44(CNR) |
| #Pages | 3 |
| Date of Issue | 2021-11-18 (SRW, SeMI, CNR) |
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