Authors:
Shunsuke Sakurai
1
;
Hideaki Uchiyama
2
;
Atshushi Shimada
1
and
Rin-Ichiro Taniguchi
1
Affiliations:
1
Graduate School and Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka Nishi-ku, Fukuoka and Japan
;
2
Library, Kyushu University, 744 Motooka Nishi-ku, Fukuoka and Japan
Keyword(s):
Deep Learning, Plant Growth, Convolutional LSTM, Frame Prediction.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
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.