ArticlePlant phenomics (Washington, D.C.)2023
Generating 3D Multispectral Point Clouds of Plants with Fusion of Snapshot Spectral and RGB-D Images.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of multi-mode analytics for enhanced plant stress evaluation.Frontiers in plant science · 2025Pooled it
- Plant counting: origins, development, and future paths.Plant phenomics (Washington, D.C.) · 2026Review
- BN-NeRF: A fast 3D reconstruction and phenotyping framework for banana plants using handheld devices.Plant phenomics (Washington, D.C.) · 2026Article
- Hyperspectral imaging meets 3D Gaussian Splatting: A novel approach beyond 3D plant morphology.Plant phenomics (Washington, D.C.) · 2026Article
- Close-Range 3D Hyperspectral Measurement System with a Physics-Guided Spectral Correction Model.Sensors (Basel, Switzerland) · 2026Article
- 2D-to-3D Image Reconstruction in Agriculture: A Review of Methods, Challenges, and AI-Driven Opportunities.Sensors (Basel, Switzerland) · 2026Review
- 3DPotatoTwin: a paired potato tuber dataset for 3D multi-sensory fusion.Plant phenomics (Washington, D.C.) · 2025Article
- Design of a binocular multispectral stereo imaging system and its application in plant phenotyping.Plant phenomics (Washington, D.C.) · 2025Article
- Phenotypic dynamics and temporal heritability of tomato architectural traits using an unmanned ground vehicle-based plant phenotyping system.Horticulture research · 2025Article
- Fusion of Multimodal Imaging and 3D Digitization Using Photogrammetry.Sensors (Basel, Switzerland) · 2024Article
- Simulation of Automatically Annotated Visible and Multi-/Hyperspectral Images Using the Helios 3D Plant and Radiative Transfer Modeling Framework.Plant phenomics (Washington, D.C.) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and high-throughput plant phenotyping is important for accelerating crop breeding. Spectral imaging that can acquire both spectral and spatial information of plants related to structural, biochemical, and physiological traits becomes one of the popular phenotyping techniques. However, close-range spectral imaging of plants could be highly affected by the complex plant structure and illumination conditions, which becomes one of the main challenges for close-range plant phenotyping. In this study, we proposed a new method for generating high-quality plant 3-dimensional multispectral point clouds. Speeded-Up Robust Features and Demons was used for fusing depth and snapshot spectral images acquired at close range. A reflectance correction method for plant spectral images based on hemisphere references combined with artificial neural network was developed for eliminating the illumination effects. The proposed Speeded-Up Robust Features and Demons achieved an average structural similarity index measure of 0.931, outperforming the classic approaches with an average structural similarity index measure of 0.889 in RGB and snapshot spectral image registration. The distribution of digital number values of the references at different positions and orientations was simulated using artificial neural network with the determination coefficient (
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Registered trials
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