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LeafLoDs: A Self-Adaptive 3-D leaf modeling with enhancing level of details expression.

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Title: LeafLoDs: A Self-Adaptive 3-D leaf modeling with enhancing level of details expression.
Authors: Hui, Zhenyang1,2,3 (AUTHOR) huizhenyang2008@ecut.edu.cn, He, Yating1,2,3 (AUTHOR) 2022120403@ecut.edu.cn, Jin, Shuanggen4,5 (AUTHOR) sgjin@shao.ac.cn, Chen, Wenbo1,2,3 (AUTHOR) 202260010@ecut.edu.cn, He, Haiqing1,2,3 (AUTHOR) hehaiqing@ecut.edu.cn, Ziggah, Yao Yevenyo6 (AUTHOR) yyziggah@umat.edu.gh
Source: Computers & Electronics in Agriculture. Mar2026, Vol. 243, pN.PAG-N.PAG. 1p.
Subjects: Three-dimensional modeling, Morphology, Leaf anatomy, Abstraction (Computer science), Foliar diagnosis, Plant development
Abstract: • A robust 3D leaf modelling method based on multi-level veins is proposed. • Multi-level veins are generated by leveraging morphological leaf traits. • Different LoDs are achieved by incorporating varying degrees of vein structures. Leaves play a crucial role in the growth of plants, both functionally and structurally. To meet the requirements of various levels of detail (LoDs) in leaf modeling for different applications, this paper introduces a self-adaptive 3D leaf modeling method aimed at enhancing LoDs representation. In this paper, a self-adaptive leaf axis determination method is first presented. According to the built leaf axis, feature points including contour points, inner corners, and outer corners are identified. Subsequently, based on these feature points, a multi-level veins generation model is proposed, extracting primary, secondary, and tertiary veins individually by leveraging the geometric and morphological traits of the leaf through a spatial colonization strategy. Hereafter, the three-dimensional leaf modeling achieves different LoDs by incorporating varying degrees of vein structures. To evaluate the effectiveness of the proposed method, both simulated and real datasets are utilized for testing. The simulated datasets consist of leaves from four different types, such as entire, toothed, disercted and digitate demonstrating that the method produces satisfactory results with small area deviation and distance residuals. In the real datasets, seven individual tomatoes with a total of 228 leaves are tested, showing that the proposed modeling approach aligns effectively with real data, with distance residuals mostly falling within -0.4 cm to 0.4 cm from real point clouds. Experimental results also reveal that higher levels of modeling lead to better outcomes due to increased detail from additional veins and feature points incorporated in the modeling process. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• A robust 3D leaf modelling method based on multi-level veins is proposed. • Multi-level veins are generated by leveraging morphological leaf traits. • Different LoDs are achieved by incorporating varying degrees of vein structures. Leaves play a crucial role in the growth of plants, both functionally and structurally. To meet the requirements of various levels of detail (LoDs) in leaf modeling for different applications, this paper introduces a self-adaptive 3D leaf modeling method aimed at enhancing LoDs representation. In this paper, a self-adaptive leaf axis determination method is first presented. According to the built leaf axis, feature points including contour points, inner corners, and outer corners are identified. Subsequently, based on these feature points, a multi-level veins generation model is proposed, extracting primary, secondary, and tertiary veins individually by leveraging the geometric and morphological traits of the leaf through a spatial colonization strategy. Hereafter, the three-dimensional leaf modeling achieves different LoDs by incorporating varying degrees of vein structures. To evaluate the effectiveness of the proposed method, both simulated and real datasets are utilized for testing. The simulated datasets consist of leaves from four different types, such as entire, toothed, disercted and digitate demonstrating that the method produces satisfactory results with small area deviation and distance residuals. In the real datasets, seven individual tomatoes with a total of 228 leaves are tested, showing that the proposed modeling approach aligns effectively with real data, with distance residuals mostly falling within -0.4 cm to 0.4 cm from real point clouds. Experimental results also reveal that higher levels of modeling lead to better outcomes due to increased detail from additional veins and feature points incorporated in the modeling process. [ABSTRACT FROM AUTHOR]
ISSN:01681699
DOI:10.1016/j.compag.2025.111377