TY - GEN
T1 - Plant growth prediction using convolutional LSTM
AU - Sakurai, Shunsuke
AU - Uchiyama, Hideaki
AU - Shimada, Atshushi
AU - Taniguchi, Rin ichiro
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85068253554&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068253554&partnerID=8YFLogxK
M3 - Conference contribution
T3 - VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
SP - 105
EP - 113
BT - VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
A2 - Kerren, Andreas
A2 - Hurter, Christophe
A2 - Braz, Jose
PB - SciTePress
T2 - 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
Y2 - 25 February 2019 through 27 February 2019
ER -