Plant Growth Prediction Based on Hierarchical Auto-encoder

Tae Hyeon Kim, Sang Ho Lee, Myung Min Oh, Jong Ok Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665409346
DOIs
Publication statusPublished - 2022
Event2022 International Conference on Electronics, Information, and Communication, ICEIC 2022 - Jeju, Korea, Republic of
Duration: 2022 Feb 62022 Feb 9

Publication series

Name2022 International Conference on Electronics, Information, and Communication, ICEIC 2022

Conference

Conference2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
Country/TerritoryKorea, Republic of
CityJeju
Period22/2/622/2/9

Keywords

  • hierarchical auto-encoder
  • plant growth prediction
  • shape domain
  • spatial transform

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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