Rapid modeling of 3D rice canopy structure considering vertical heterogeneity and analysis of spectral response.

Juchi Bai, Youwen Tian, Yinxuan Hui, Rui Wu, Fenghua Yu, Chunshan Wang

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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.

Juchi BaiCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, 110866, China.

Youwen TianCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, 110866, China.

Yinxuan HuiCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, 110866, China.

Rui WuCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, 110866, China.

Fenghua YuCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, 110866, China.

Chunshan WangSchool of Information Science and Technology, Hebei Agricultural University, Baoding, 071001, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The vertical heterogeneity of rice canopy structure limits the accuracy of inverting leaf physicochemical parameters using traditional radiative transfer models, while LiDAR-based 3D reconstruction remains costly for large-scale applications. To address these challenges, this study proposes a method for constructing 3D rice canopy scenes using "Precision Mode" and "Rapid Mode" strategies. The Precision Mode builds detailed structural models based on measured morphological parameters, validated via the LESS 3D radiative transfer model. To overcome the limitations of obtaining detailed morphology via UAV remote sensing, the Rapid Mode employs machine learning algorithms-specifically Support Vector Machine (SVM), Random Forest (RF), and XGBoost-to map easily accessible parameters (LAI, Above-ground Biomass, Plant Height, and Transplanting Date) to detailed 3D structural parameters. Results indicate that XGBoost achieves the highest accuracy in the Rapid Mode. Furthermore, simulated spectra under both modes showed high consistency with measured spectra, yielding average RMSE values of 0.0104 (R

Indexed as

3D radiative transfer modelCanopy spectraMachine learningRiceVertical heterogeneity

Identifiers

PMID42254595

DOI10.1016/j.plaphe.2026.100226

PMCPMC13235349

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