Lettuce Fresh Weight Prediction in a Plant Factory Using Plant Growth Models

Hosoda, Yuya; Tada, Tota; Goto, Hitoshi

doi:https://doi.org/10.1109/ACCESS.2024.3423455

Lettuce Fresh Weight Prediction in a Plant Factory Using Plant Growth Models

Hosoda, Yuya; Tada, Tota; Goto, Hitoshi

IEEE Access, 2024, 12, 97226-97234

アクセス数:144件（2025-07-16 08:48 集計）

固定URL: https://hdl.handle.net/11094/98251

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タイトル	Lettuce Fresh Weight Prediction in a Plant Factory Using Plant Growth Models
著者	NRID 1000000964107 1000000964107 researchmap permalink hosoda00964107 hosoda00964107 ORCID 0000-0003-0687-4326 0000-0003-0687-4326 著者名 Hosoda, Yuya
	著者名 Tada, Tota
	NRID 1000060282042 1000060282042 著者名 Goto, Hitoshi
内容	Hosoda Y., Tada T., Goto H.. Lettuce Fresh Weight Prediction in a Plant Factory Using Plant Growth Models. IEEE Access 12, 97226 (2024); https://doi.org/10.1109/ACCESS.2024.3423455.
抄録	This paper proposes a novel method to predict the fresh weight of lettuce at the shipping stage in a plant factory using the early-stage growth images. It is well-established that the size and shape of plants correlate with their fresh weight. The proposed method captures chlorophyll fluorescence-based growth images daily and extracts geometric features such as projection area, edge length, and skeleton length. We design a regression model to predict the fresh weight using the dimensionality-reduced historical features. However, without considering growth statuses, the dimensionality reduction approach leads to decreased predictive performance for mature and slower-growing plants. In this paper, we generate a plant growth model that simulates the growth process by integrating multiple growth records based on the comparison of growth statuses. The proposed method then reduces the dimensionality by fitting historical features to the plant growth model to obtain future features. Experimental results demonstrate that the proposed method accurately predicts the fresh weight and achieves the coefficient of determination of 0.885, root mean square error of 8.790 g, and mean absolute error of 6.684 g when predicting the fresh weight ten days ahead using growth images from the past ten days.
出版者	Institute of Electrical and Electronics Engineers Inc.
収録物名	IEEE Access
巻(号)	12
ページ	97226-97234
刊行年月	2024-07-23
言語	英語
ハンドルURL	https://hdl.handle.net/11094/98251
eISSN	21693536
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1109/ACCESS.2024.3423455
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCTERMS.bibliographicCitation	IEEE Access. 2024, 12, p. 97226-97234
DC.title	Lettuce Fresh Weight Prediction in a Plant Factory Using Plant Growth Models
DC.creator	Hosoda, Yuya
	Tada, Tota
	Goto, Hitoshi
DC.publisher	Institute of Electrical and Electronics Engineers Inc.
DC.language' scheme='DCTERMS.RFC1766	英語
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-07-23
DC.identifier	https://doi.org/10.1109/ACCESS.2024.3423455
DC.description	Hosoda Y., Tada T., Goto H.. Lettuce Fresh Weight Prediction in a Plant Factory Using Plant Growth Models. IEEE Access 12, 97226 (2024); https://doi.org/10.1109/ACCESS.2024.3423455.
DCTERMS.abstract	This paper proposes a novel method to predict the fresh weight of lettuce at the shipping stage in a plant factory using the early-stage growth images. It is well-established that the size and shape of plants correlate with their fresh weight. The proposed method captures chlorophyll fluorescence-based growth images daily and extracts geometric features such as projection area, edge length, and skeleton length. We design a regression model to predict the fresh weight using the dimensionality-reduced historical features. However, without considering growth statuses, the dimensionality reduction approach leads to decreased predictive performance for mature and slower-growing plants. In this paper, we generate a plant growth model that simulates the growth process by integrating multiple growth records based on the comparison of growth statuses. The proposed method then reduces the dimensionality by fitting historical features to the plant growth model to obtain future features. Experimental results demonstrate that the proposed method accurately predicts the fresh weight and achieves the coefficient of determination of 0.885, root mean square error of 8.790 g, and mean absolute error of 6.684 g when predicting the fresh weight ten days ahead using growth images from the past ten days.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	98251
citation_title	Lettuce Fresh Weight Prediction in a Plant Factory Using Plant Growth Models
citation_author	Hosoda, Yuya
	Tada, Tota
	Goto, Hitoshi
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	英語
citation_date	2024-07-23
citation_journal_title	IEEE Access
citation_volume	12
citation_firstpage	97226
citation_lastpage	97234
citation_doi	https://doi.org/10.1109/ACCESS.2024.3423455
citation_public_url	https://hdl.handle.net/11094/98251

Lettuce Fresh Weight Prediction in a Plant Factory Using Plant Growth Models

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