Three-Dimensional Plant Model Development Through Image Recognition

Hosoda, Yuya; Bilguun, Ganzurkh; Oboshi, Jin; Goto, Hitoshi

doi:https://doi.org/10.1109/ACCESS.2024.3507916

Three-Dimensional Plant Model Development Through Image Recognition

Hosoda, Yuya; Bilguun, Ganzurkh; Oboshi, Jin et al.

IEEE Access, 2024, 12, 185557-185566

アクセス数:178件（2026-02-16 03:44 集計）

固定URL: https://hdl.handle.net/11094/100432

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タイトル	Three-Dimensional Plant Model Development Through Image Recognition
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 Bilguun, Ganzurkh
	著者名 Oboshi, Jin
	NRID 1000060282042 1000060282042 著者名 Goto, Hitoshi
キーワード等	Computer vision
	image recognition
	plant
	three-dimensional model
	vector search
内容	Hosoda Y., Bilguun G., Oboshi J., et al. Three-Dimensional Plant Model Development Through Image Recognition. IEEE Access 12, 185557 (2024); https://doi.org/10.1109/ACCESS.2024.3507916.
抄録	This paper proposes a novel method using image-recognition techniques to develop three-dimensional (3D) models of basil plants. Traditional approaches have dificulty scanning outdoor plants and stems overlapping outer leaves. In this paper, we collect 3D plant models in advance that reproduce the external and internal structures. Then, by selecting from the database the 3D model most similar to the actual plant in appearance, the proposed method develops 3D plant models using only images. However, collecting precise 3D models is a cost-intensive task. Based on the growth pattern that basil plants exhibit alternating leaves during growth, the proposed method automatically mass-produces realistic 3D plant models by assembling 3D leaf and stem models of actual plants. Additionally, we employ an image-recognition technique to extract embedding vectors from multi-angle images and assess the visual similarity between the actual plant and the realistic 3D plant model based on their cosine similarity. Finally, we construct a vector-search system incorporating k-means clustering and dimensionality reduction to limit the search scope and minimize computational complexity. Experimental results show that the proposed method efficiently obtains the most similar 3D model in the database, achieving a mean reciprocal rank of 0.90 and a search time of 0.003 s per query.
出版者	Institute of Electrical and Electronics Engineers Inc.
収録物名	IEEE Access
巻(号)	12
ページ	185557-185566
刊行年月	2024-12-17
言語	英語
ハンドルURL	https://hdl.handle.net/11094/100432
eISSN	21693536
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1109/ACCESS.2024.3507916
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. 185557-185566
DC.title	Three-Dimensional Plant Model Development Through Image Recognition
DC.creator	Hosoda, Yuya
	Bilguun, Ganzurkh
	Oboshi, Jin
	Goto, Hitoshi
DC.publisher	Institute of Electrical and Electronics Engineers Inc.
DC.language' scheme='DCTERMS.RFC1766	英語
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-12-17
DC.identifier	https://doi.org/10.1109/ACCESS.2024.3507916
DC.subject	Computer vision
	image recognition
	plant
	three-dimensional model
	vector search
DC.description	Hosoda Y., Bilguun G., Oboshi J., et al. Three-Dimensional Plant Model Development Through Image Recognition. IEEE Access 12, 185557 (2024); https://doi.org/10.1109/ACCESS.2024.3507916.
DCTERMS.abstract	This paper proposes a novel method using image-recognition techniques to develop three-dimensional (3D) models of basil plants. Traditional approaches have dificulty scanning outdoor plants and stems overlapping outer leaves. In this paper, we collect 3D plant models in advance that reproduce the external and internal structures. Then, by selecting from the database the 3D model most similar to the actual plant in appearance, the proposed method develops 3D plant models using only images. However, collecting precise 3D models is a cost-intensive task. Based on the growth pattern that basil plants exhibit alternating leaves during growth, the proposed method automatically mass-produces realistic 3D plant models by assembling 3D leaf and stem models of actual plants. Additionally, we employ an image-recognition technique to extract embedding vectors from multi-angle images and assess the visual similarity between the actual plant and the realistic 3D plant model based on their cosine similarity. Finally, we construct a vector-search system incorporating k-means clustering and dimensionality reduction to limit the search scope and minimize computational complexity. Experimental results show that the proposed method efficiently obtains the most similar 3D model in the database, achieving a mean reciprocal rank of 0.90 and a search time of 0.003 s per query.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	100432
citation_title	Three-Dimensional Plant Model Development Through Image Recognition
citation_author	Hosoda, Yuya
	Bilguun, Ganzurkh
	Oboshi, Jin
	Goto, Hitoshi
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	英語
citation_date	2024-12-17
citation_journal_title	IEEE Access
citation_volume	12
citation_firstpage	185557
citation_lastpage	185566
citation_doi	https://doi.org/10.1109/ACCESS.2024.3507916
citation_public_url	https://hdl.handle.net/11094/100432

Three-Dimensional Plant Model Development Through Image Recognition

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