Plant Growth Prediction using Convolutional LSTM

Shunsuke Sakurai, Hideaki Uchiyama, Atshushi Shimada, Rin-Ichiro Taniguchi

Abstract

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.

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Paper Citation


in Harvard Style

Sakurai S., Uchiyama H., Shimada A. and Taniguchi R. (2019). Plant Growth Prediction using Convolutional LSTM.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 105-113. DOI: 10.5220/0007404901050113


in Bibtex Style

@conference{visapp19,
author={Shunsuke Sakurai and Hideaki Uchiyama and Atshushi Shimada and Rin-Ichiro Taniguchi},
title={Plant Growth Prediction using Convolutional LSTM},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={105-113},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007404901050113},
isbn={978-989-758-354-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Plant Growth Prediction using Convolutional LSTM
SN - 978-989-758-354-4
AU - Sakurai S.
AU - Uchiyama H.
AU - Shimada A.
AU - Taniguchi R.
PY - 2019
SP - 105
EP - 113
DO - 10.5220/0007404901050113