Hyperspectral imaging meets 3D Gaussian Splatting: A novel approach beyond 3D plant morphology.

Wenzhe Deng, Tingting Wu, Zhichao Ni, Yajie Liu, Hanyue Jia, Qingping Ling

Abstract read

In one paragraph

Article in Plant phenomics (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Wenzhe DengCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China.

Tingting WuCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China.

Zhichao NiCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China.

Yajie LiuCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China.

Hanyue JiaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China.

Qingping LingCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate acquisition of plant phenotypes is crucial for elucidating plant growth and development, underlying genetic mechanisms, and responses to environmental stimuli. Traditional three-dimensional (3D) phenotyping mainly captures geometric traits such as height, leaf area, and canopy volume, while overlooking physiological and biochemical information. Here, we present a hyperspectral point clouds generation method based on PlantGaussian (a 3D Gaussian Splatting technique) that integrates structural and spectral information, extending 3D phenotyping beyond geometry to include physiology. High-quality plant point clouds were first reconstructed using PlantGaussian, and hyperspectral images(HSI) were mapped onto them to produce hyperspectral point clouds. In potted soybean experiments, we built predictive models linking hyperspectral reflectance to SPAD (chlorophyll content) and EWT (equivalent water thickness), and visualized their 3D distributions. The hyperspectral point clouds achieved strong predictive performance for SPAD (

Indexed as

3D Gaussian splattingHyperspectral imagingHyperspectral point cloudsMultimodal data fusionPlant phenotyping

Identifiers

PMID42382241

DOI10.1016/j.plaphe.2026.100198

PMCPMC13316395

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.