Predicting grain yield and designing density-tolerant maize ideotypes through 3D architectural phenotyping at silking

Abstract

Accurate prediction of maize (Zea mays L.) yield and design of density-tolerant ideotypes are crucial for crop management and yield improvement. Three-dimensional (3D) phenotypic traits are closely related to light interception efficiency and yield formation, theoretically serving as important indicators for yield prediction and plant architecture optimization. However, studies utilizing 3D phenotypic traits for yield prediction and ideotype design remain limited. In this study, a two-year (2023-2024) field experiment was conducted with 10 maize hybrids grown under three planting densities (37,500 (low density, LD), 67,500 (medium density, MD), and 97,500 (high density, HD) plants ha⁻¹). Plant 3D phenotypic traits at the silking stage were captured using the MVS-Pheno platform. The results showed that increasing planting density led to more compact plant architecture and significant changes in 3D phenotypic traits. A partial least squares regression model integrating 3D phenotypic traits with canopy light interception data achieved high prediction accuracy for yield (R² ​= ​0.91, RMSE ​= ​0.49 ​Mg ​ha⁻¹). Feature sensitivity and correlation analyses further identified projected area (PJA) and plant side width (PSW) as critical indicators for designing varieties tolerant to high density. Furthermore, a strategy was proposed to match plant ideotype to different planting densities: under MD, the leaf area per plant (LAP) and PJA increased, whereas the PSW and leaf orientation value (LOV) decreased; under HD, the LAP, PJA, and PSW decreased, whereas the LOV increased. These findings provide an effective model for yield prediction and a valuable reference for breeding maize with optimal architecture for high-density cultivation.

Keywords

3D phenotypic trait / Grain yield / Ideotype / Maize / Plant architecture / Planting density

Abbreviations

3D	three-dimensional
AA	azimuth area
CH	convex hull volume
CLIB	canopy light interception rate at the bottom layer
CLIE	canopy light interception rate at the ear layer
CR	compact ratio azimuth
GW	100-grain weight
HD	high density (97,500 plants ha−1)
HMLA	plant height of the median leaf area
HY	high-yield group
IPAR	canopy light interception rate
KN	kernel number per unit area
KNN	k-nearest neighbors
KNP	kernel number per ear
LA	leaf angle
LAB	leaf angle below the ear
LAE	leaf angle on the ear
LAP	leaf area per plant
LAU	leaf angle above the ear
LAW	leaf angle for the whole plant
LD	low density (37,500 plants ha−1)
LOV	leaf orientation value
LOVB	leaf orientation value below the ear
LOVE	leaf orientation value on the ear
LOVU	leaf orientation value above the ear
LOVW	leaf orientation value for the whole plant
LY	low-yield group
Max	maximum
MD	medium density (67,500 plants ha−1)
Med	median
Min	minimum
MY	medium-yield group
NVP	number of voxel volume plant
PAR	projected area rectangle
PH	plant height
PJA	projected area
PJAL	projected lower layer plant area
PJAM	projected middle layer plant area
PJAU	projected upper layer plant area
PLSR	partial least squares regression
PSW	plant side width
PW	plant width
SVR	support vector regression
UAV	unmanned aerial vehicle

Authors' contribution

Guangtao Wang: Writing-review & editing, Writing-original draft, Visualization, Validation, Methodology, Investigation, Data curation. Guanmin Huang: Writing-review & editing, Methodology, Funding acquisition. Sheng Wu: Software, Methodology. Hongguang Cai: Data curation. Wenlang Hu: Data curation. Bo Chen: Investigation, Data curation. Baiyan Wang: Investigation, Data curation. Xianju Lu: Investigation, Data curation. Chunjiang Zhao: Funding acquisition, Conceptualization. Xinyu Guo: Writing-review & editing, Supervision, Funding acquisition, Conceptualization.

Availability of data and materials

Not applicable.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was supported by State Key Program of National Natural Science Foundation of China (32330075), National Key Research and Development Program (2022YFD1900701), Construction of Collaborative Innovation Center of Beijing Academy of Agriculture and Forestry Sciences (KJCX20240406), and China Postdoctoral Science Foundation (2023M730314).

Appendix A. Supplementarydata

Supplementary data to this article can be found online at https://doi.org/10.1016/j.crope.2025.09.003.

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