Three- dimensional modeling and visualization of rice root system based on the improved dual-scale automaton and L-system

Yang, Lu; Wu, Panpan; Liao, Mingfu; Peng, Jun; TANG, Zizong; Long, Huibin; Zhang, Huanhuan; Yu, Xiaoyun

doi:10.1016/j.compag.2022.106823

Cited by 7 publications

(1 citation statement)

References 40 publications

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“…In terms of plant 3D modelling, there are four main categories: rule-based methods ( Hidayat et al 2020 ; Cieslak et al 2022 ; Yang et al 2022 ), 3D digitizer-based methods ( Wang et al 2018 ; Chenxi et al 2022 ), image-based methods ( Lou et al 2014 ; Wu et al 2018 ; Das Choudhury et al 2020 ; Lu et al 2021 ; Wu et al 2022 ) and laser scanning-based methods ( Ma et al 2019 ; Sun and Wang 2019 ; Jin et al 2021 ; Sun et al 2021 ). Among them, the rule-based method can realize the 3D growth modelling of plants by specifying rules and parameterization.…”

Section: Introductionmentioning

confidence: 99%

Using high-throughput phenotype platform MVS-Pheno to reconstruct the 3D morphological structure of wheat

Li,

Wu,

Wen

et al. 2024

AoB PLANTS

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It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of crop plants. Automatic acquisition of 3D information is one of the key steps in plant morphological structure research. Taking wheat as the research object, we propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale. Specifically, we use the MVS-Pheno platform to reconstruct the point cloud of wheat plants and segment organs through the deep learning algorithm. On this basis, we automatically reconstructed the 3D structure of leaves and tillers and extracted the morphological parameters of wheat. The results show that the semantic segmentation accuracy of organs is 95.2%, and the instance segmentation accuracy AP50 is 0.665. The R2 values for extracted leaf length, leaf width, leaf attachment height, stem leaf angle, tiller length, and spike length were 0.97, 0.80, 1.00, 0.95, 0.99, and 0.95, respectively. This method can significantly improve the accuracy and efficiency of 3D morphological analysis of wheat plants, providing strong technical support for research in fields such as agricultural production optimization and genetic breeding.

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