Plant growth prediction using convolutional LSTM

Shunsuke Sakurai, Hideaki Uchiyama, Atshushi Shimada, Rin ichiro Taniguchi

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

21 被引用数 (Scopus)

抄録

This paper presents a method for predicting plant growth in future images from past images, as a new phenotyping technology. This is achieved by modeling the representation of plant growth based on neural network. In order to learn the long-term dependencies in plant growth from the images, we propose to employ a Convolutional LSTM based framework. Especially, We apply an encoder-decoder model inspired by a framework on future frame prediction to model the representation of plant growth effectively. In addition, we propose two additional loss terms to put the constraints on shape changes of leaves between consecutive images. In the evaluation, we demonstrated the effectiveness of the proposed loss functions through the comparisons using labeled plant growth images.

本文言語英語
ホスト出版物のタイトルVISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
編集者Andreas Kerren, Christophe Hurter, Jose Braz
出版社SciTePress
ページ105-113
ページ数9
ISBN(電子版)9789897583544
出版ステータス出版済み - 1月 1 2019
イベント14th International Conference on Computer Vision Theory and Applications, VISAPP 2019 - Part of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019 - Prague, チェコ共和国
継続期間: 2月 25 20192月 27 2019

出版物シリーズ

名前VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
5

会議

会議14th International Conference on Computer Vision Theory and Applications, VISAPP 2019 - Part of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019
国/地域チェコ共和国
CityPrague
Period2/25/192/27/19

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識

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