TY - GEN
T1 - Plant Growth Prediction Based on Hierarchical Auto-encoder
AU - Kim, Tae Hyeon
AU - Lee, Sang Ho
AU - Oh, Myung Min
AU - Kim, Jong Ok
N1 - Funding Information:
This work is supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A4A4079705) and supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-2018-0-01421) supervised by the IITP(Institute of Information communications Technology Planning Evaluation).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - As plants grow, the area of the leaves changes arbitrarily and the growth rate varies from leaf to leaf. In controlled environments such as plant factories, accurate plant growth prediction models are required for efficient cultivation. In this paper, we propose a new deep learning network that can predict plant growth. First, for predicting the shape of a plant, hierarchical auto-encoders are adopted for shape prediction. After the plant shape is predicted first, its RGB information is replenished by fusing the shape with a current RGB image to generate a future RGB plant image. A variety of experiments have been performed with a dataset produced from a plant factory. Experimental results show that the proposed method is resistant to predicting global and local growth of plant leaves. It also predicts dynamic plant movements well, leading to the accurate prediction of a future plant image.
AB - As plants grow, the area of the leaves changes arbitrarily and the growth rate varies from leaf to leaf. In controlled environments such as plant factories, accurate plant growth prediction models are required for efficient cultivation. In this paper, we propose a new deep learning network that can predict plant growth. First, for predicting the shape of a plant, hierarchical auto-encoders are adopted for shape prediction. After the plant shape is predicted first, its RGB information is replenished by fusing the shape with a current RGB image to generate a future RGB plant image. A variety of experiments have been performed with a dataset produced from a plant factory. Experimental results show that the proposed method is resistant to predicting global and local growth of plant leaves. It also predicts dynamic plant movements well, leading to the accurate prediction of a future plant image.
KW - hierarchical auto-encoder
KW - plant growth prediction
KW - shape domain
KW - spatial transform
UR - http://www.scopus.com/inward/record.url?scp=85128846408&partnerID=8YFLogxK
U2 - 10.1109/ICEIC54506.2022.9748287
DO - 10.1109/ICEIC54506.2022.9748287
M3 - Conference contribution
AN - SCOPUS:85128846408
T3 - 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
BT - 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
Y2 - 6 February 2022 through 9 February 2022
ER -