A study on response of planting density to 3D plant shape plasticity and population light transmittance of maize

doi:10.1016/j.jia.2025.05.011

Advanced Online Publication | Current Issue | Archive | Adv Search

Guangtao Wang^1,², Guanmin Huang², Weiliang Wen², Sheng Wu², Xianju Lu², Bo Chen², Xinming Ma^1#, Xinyu Guo^2#, Chunjiang Zhao^1,^2#

¹College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China

²Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China

Highlights

1. During vegetative growth, maize exhibited enhanced vertical and horizontal development under increased planting density.

2. At silking stage, plants showed reduced spatial occupancy with pronounced lateral growth inhibition under increased planting density.

3. The light transmission model based on support vector regression achieved reliable prediction accuracy.

Abstract
References

Download: PDF in ScienceDirect
Export: BibTeX | EndNote (RIS)

摘要

传统的二维分析在研究不同种植密度下玉米（Zea mays. L）株形可塑性及冠层透光率时，由于难以捕捉空间异质性而存在局限性。本研究利用运动恢复结构技术和多视角三维（three-dimensional，3D）表型平台探究不同玉米品种和种植密度下的株形可塑性。开发了7种新的3D结构参数，并构建了3D冠层模型用于光分布仿真。在V9时期，与低密度（37500株/公顷，LD）相比，中密度（67500株/公顷，MD）使植株侧宽和凸包体积分别增加7.2%和11.4%，高密度（97500株/公顷，HD）较MD增加4.2%和17.8%。在V13时期，保持了一致的变化。在吐丝期，体素体积（number of voxel volume plant，NVP）和投影面积（projected area，PJA）下降6.2%和11.9%（从LD到MD）以及4.9%和3.6%（从MD到HD）。不同密度下，MC812和JNK728的PJA比ZD958分别低17.2-20.0%和6.2-7.6%，NVP分别低20.0-26.5%和15.4-21.1%。结合点云参数和支持向量回归构建的底部透光率估算模型具有较高预测精度（R²=0.76，RMSE=2.89%）。3D冠层模型能较好地模拟群体光分布（R²=0.83，RMSE=8.53%）。NVP和PJA是影响冠层底部透光率的关键参数，可作为玉米耐密性育种的3D选择指标。这些发现深入揭示了特定阶段的结构可塑性和光截获，为耐密玉米的分子设计育种提供了支持。

Abstract

Traditional two-dimensional analyses of maize (Zea mays. L) plant architecture plasticity and canopy light transmission under varying planting densities have limitations in capturing spatial heterogeneity. This study utilized structure-from-motion technology with a multi-view three-dimensional (3D) phenotyping platform to investigate architectural plasticity across different maize varieties and planting densities. Seven novel 3D architectural parameters were developed, and 3D canopy models were constructed for light distribution simulation. At V9 stage, medium planting density (67,500 plants ha⁻¹, MD) increased plant side width and convex hull volume by 7.2 and 11.4%, respectively, compared to low planting density (37,500 plants ha⁻¹, LD). High planting density (97,500 plants ha⁻¹, HD) increased by 4.2 and 17.8% compared with MD. Similar changes were maintained at V13 stage. At silking stage, number of voxel volume plant (NVP) and projected area (PJA) decreased by 6.2 and 11.9% (LD to MD), and 4.9 and 3.6% (MD to HD). Under different densities, MC812 and JNK728 showed 17.2-20.0% and 6.2-7.6% decrease in PJA, and 20.0-26.5% and 15.4-21.1% decrease in NVP compared to ZD958. A bottom light transmittance estimation model combining point cloud parameters with support vector regression achieved reliable prediction (R²=0.76, RMSE=2.89%). The 3D canopy model effectively simulated population light distribution (R²=0.83, RMSE=8.53%). NVP and PJA were identified as critical parameters affecting bottom canopy light transmittance, suggesting their potential as 3D selection indices for maize density tolerance breeding. These findings provide insights into stage-specific architectural plasticity and light interception, supporting molecular design breeding of density-tolerant maize.

Keywords: maize planting density 3D phenotyping light transmittance 3D canopy model

Online: 16 May 2025

Fund:

The work was funded by Construction of Collaborative Innovation Center of Beijing Academy of Agricultural and Forestry Sciences (KJCX20240406), State Key Program of National Natural Science Foundation of China (32330075) and China Postdoctoral Science Foundation (2023M730314).

About author: #Correspondence Xinming Ma, E-mail: xinmingma@126.com; Xinyu Guo, E-mail: guoxy73@163.com; Chunjiang Zhao, E-mail: zhaocj@nercita.org.cn

	Service
	E-mail this article
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

Guangtao Wang, Guanmin Huang, Weiliang Wen, Sheng Wu, Xianju Lu, Bo Chen, Xinming Ma, Xinyu Guo, Chunjiang Zhao. 2025. A study on response of planting density to 3D plant shape plasticity and population light transmittance of maize. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2025.05.011

Bittner S, Gayler S, Biernath C, Winkler J B, Seifert S, Pretzsch H, Priesack E. 2012. Evaluation of a ray-tracing canopy light model based on terrestrial laser scans. Canadian Journal of Remote Sensing, 38, 619–628.

Chang T G, Shi Z, Zhao H, Song Q, He Z, Van Rie J, Den Boer B, Galle A, Zhu X G. 2022. 3dCAP-Wheat: An open-source comprehensive computational framework precisely quantifies wheat foliar, nonfoliar, and canopy photosynthesis. Plant Phenomics, 2022, 28-46.

Chen H, Zhang M, Xiao S, Wang Q, Cai Z, Dong Q, Feng P, Shao K, Ma Y. 2024. Quantitative analysis and planting optimization of multi-genotype sugar beet plant types based on 3D plant architecture. Computers and Electronics in Agriculture, 225, 109231.

Chen T W, Cabrera-Bosquet L, Alvarez Prado S, Perez R, Artzet S, Pradal C, Coupel-Ledru A, Fournier C, Tardieu F. 2019. Genetic and environmental dissection of biomass accumulation in multi-genotype maize canopies. Journal of Experimental Botany, 70, 2523–2534.

Ci X, Li M, Xu J, Lu Z, Bai P, Ru G, Liang X, Zhang D, Li X, Bai L, Xie C, Hao Z, Zhang S, Dong S. 2012. Trends of grain yield and plant traits in Chinese maize cultivars from the 1950s to the 2000s. Euphytica, 185, 395–406.

Duvick D N. 2005. The contribution of breeding to yield advances in maize (Zea mays L.). Advances in Agronomy, 86, 83–145.

Feldman A, Wang H, Fukano Y, Kato Y, Ninomiya S, Guo W. 2021. EasyDCP: An affordable, high‐throughput tool to measure plant phenotypic traits in 3D. Methods in Ecology and Evolution, 12, 1679–1686.

Gitelson A, Viña A, Solovchenko A, Arkebauer T, Inoue Y. 2019. Derivation of canopy light absorption coefficient from reflectance spectra. Remote Sensing of Environment, 231, 111276.

Gou L, Xue J, Qi B, Ma B, Zhang W. 2017. Morphological variation of maize cultivars in response to elevated plant densities. Agronomy Journal, 109, 1443–1453.

Gu S, Wen W, Xu T, Lu X, Yu Z, Guo X, Zhao C. 2022. Use of 3D modeling to refine predictions of canopy light utilization: A comparative study on canopy photosynthesis models with different dimensions. Frontiers in Plant Science, 13, 735981.

Hui F, Zhu J, Hu P, Meng L, Zhu B, Guo Y, Li B, Ma Y. 2018. Image-based dynamic quantification and high-accuracy 3D evaluation of canopy structure of plant populations. Annals of Botany, 121, 1079–1088.

Klodt M, Cremers D. 2015. High-resolution plant shape measurements from multi-view stereo reconstruction. In: Agapito L, Bronstein M M, Rother C, eds., Computer Vision-ECCV 2014 Workshops. Lecture Notes in Computer Science, Springer International Publishing, Cham. pp. 174–184.

Li D H. 2000. Retrospect and prospect in breeding of compact corn. Crops, 5, 1–5. (in Chinese)

Li R, Hu D, Ren H, Yang Q, Dong S, Zhang J, Zhao B, Liu P. 2022. How delaying post-silking senescence in lower leaves of maize plants increases carbon and nitrogen accumulation and grain yield. The Crop Journal, 10, 853–863.

Li R, Zhang G, Liu G, Wang K, Xie R, Hou P, Ming B, Wang Z, Li S. 2021. Improving the yield potential in maize by constructing the ideal plant type and optimizing the maize canopy structure. Food and Energy Security, 10, e312.

Li Y, Liu J, Zhang B, Wang Y, Yao J, Zhang X, Fan B, Li X, Hai Y, Fan X. 2022. Three-dimensional reconstruction and phenotype measurement of maize seedlings based on multi-view image sequences. Frontiers in Plant Science, 13, 974339.

Liu G, Hou P, Xie R, Ming B, Wang K, Xu W, Liu W, Yang Y, Li S. 2017. Canopy characteristics of high-yield maize with yield potential of 22.5 Mg ha⁻¹. Field Crops Research, 213, 221–230.

Liu G, Liu W, Hou P, Ming B, Yang Y, Guo X, Xie R, Wang K, Li S. 2021. Reducing maize yield gap by matching plant density and solar radiation. Journal of Integrative Agriculture, 20, 363–370.

Liu Y, Jafari F, Wang H. 2021. Integration of light and hormone signaling pathways in the regulation of plant shade avoidance syndrome. aBIOTECH, 2, 131–145.

Ma D, Xie R, Yu X, Li S, Gao J. 2022. Historical trends in maize morphology from the 1950s to the 2010s in China. Journal of Integrative Agriculture, 21, 2159–2167.

Maddonni G A, Chelle M, Drouet J L, Andrieu B. 2001. Light interception of contrasting azimuth canopies under square and rectangular plant spatial distributions: Simulations and crop measurements. Field Crops Research, 70, 1–13.

Markham M Y, Stoltenberg D E. 2010. Corn morphology, mass, and grain yield as affected by early-season red: Far-red light environments. Crop Science, 50, 273–280.

Postma J A, Hecht V L, Hikosaka K, Nord E A, Pons T L, Poorter H. 2021. Dividing the pie: A quantitative review on plant density responses. Plant, Cell & Environment, 44, 1072–1094.

Pound M P, French A P, Murchie E H, Pridmore T P. 2014. Automated recovery of three-dimensional models of plant shoots from multiple color images. Plant Physiology, 166, 1688–1698.

Van Roekel R J, Coulter J A. 2011. Agronomic responses of corn to planting date and plant density. Agronomy Journal, 103, 1414–1422.

Shi Q, Xia Y, Wang Q, Lv K, Yang H, Cui L, Sun Y, Wang X, Tao Q, Song X, Xu D, Xu W, Wang X, Wang X, Kong F, Zhang H, Li B, Li P, Wang H, Li G. 2024. Phytochrome B interacts with LIGULELESS1 to control plant architecture and density tolerance in maize. Molecular Plant, 17, 1255–1271.

Song Q, Liu F, Bu H, Zhu X G. 2023. Quantifying contributions of different factors to canopy photosynthesis in 2 maize varieties: Development of a novel 3D canopy modeling pipeline. Plant Phenomics, 5, 0075.

Taube F, Vogeler I, Kluß C, Herrmann A, Hasler M, Rath J, Loges R, Malisch C S. 2020. Yield progress in forage maize in NW Europe—Breeding progress or climate change effects? Frontiers in Plant Science, 11, 1214.

Tian J, Wang C, Xia J, Wu L, Xu G, Wu W, Li D, Qin W, Han X, Chen Q, Jin W, Tian F. 2019. Teosinte ligule allele narrows plant architecture and enhances high-density maize yields. Science, 365, 658–664.

Vazin F, Hassanzadeh M, Madani A, Nassiri-Mahallati M, Nasri M. 2010. Modeling light interception and distribution in mixed canopy of common cocklebur (Xanthium stramarium L.) in competition with corn. Planta Daninha, 28, 455–462.

Wang B, Lin Z, Li X, Zhao Y, Zhao B, Wu G, Ma X, Wang H, Xie Y, Li Q, Song G, Kong D, Zheng Z, Wei H, Shen R, Wu H, Chen C, Meng Z, Wang T, Li Y, et al. 2020. Genome-wide selection and genetic improvement during modern maize breeding. Nature Genetics, 52, 565–571.

Welcker C, Spencer N A, Turc O, Granato I, Chapuis R, Madur D, Beauchene K, Gouesnard B, Draye X, Palaffre C, Lorgeou J, Melkior S, Guillaume C, Presterl T, Murigneux A, Wisser R J, Millet E J, Van Eeuwijk F, Charcosset A, Tardieu F. 2022. Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions. Nature Communications, 13, 3225.

Wen W, Guo X, Li B, Wang C, Wang Y, Yu Z, Wu S, Fan J, Gu S, Lu X. 2019. Estimating canopy gap fraction and diffuse light interception in 3D maize canopy using hierarchical hemispheres. Agricultural and Forest Meteorology, 276–277, 107594.

Wen W, Wang Y, Wu S, Liu K, Gu S, Guo X. 2021. 3D phytomer-based geometric modelling method for plants—the case of maize. AoB Plants, 13, plab055.

Wu S, Wen W, Wang Y, Fan J, Wang C, Gou W, Guo X. 2020. MVS-Pheno: A portable and low-cost phenotyping platform for maize shoots using multiview stereo 3D reconstruction. Plant Phenomics, 2020, 1848437.

Wu S, Zhang Y, Zhao Y, Wen W, Wang C, Lu X, Guo M, Guo X, Zhao J, Zhao C. 2024. Using high-throughput phenotyping platform MVS-Pheno to decipher the genetic architecture of plant spatial geometric 3D phenotypes for maize. Computers and Electronics in Agriculture, 225, 109259.

Wu Y, Wen W, Gu S, Huang G, Wang C, Lu X, Xiao P, Guo X, Huang L. 2024. Three-dimensional modeling of maize canopies based on computational intelligence. Plant Phenomics, 6, 0160.

Xiang L, Bao Y, Tang L, Ortiz D, Salas-Fernandez M G. 2019. Automated morphological traits extraction for sorghum plants via 3D point cloud data analysis. Computers and Electronics in Agriculture, 162, 951–961.

Xiao S, Chai H, Shao K, Shen M, Wang Q, Wang R, Sui Y, Ma Y. 2020. Image-based dynamic quantification of aboveground structure of sugar beet in field. Remote Sensing, 12, 269.

Xiao S, Fei S, Li Q, Zhang B, Chen H, Xu D, Cai Z, Bi K, Guo Y, Li B, Chen Z, Ma Y. 2023. The importance of using realistic 3D canopy models to calculate light interception in the field. Plant Phenomics, 5, 0082.

Xue H, Han Y, Li Y, Wang G, Feng L, Fan Z, Du W, Beifang Yang, Cao C, Mao S. 2015. Spatial distribution of light interception by different plant population densities and its relationship with yield. Field Crops Research, 184, 17–27.

Xue J, Xie R, Zhang W, Wang K, Hou P, Ming B, Gou L, Li S. 2017. Research progress on reduced lodging of high-yield and -density maize. Journal of Integrative Agriculture, 16, 2717–2725.

Yan Y, Duan F, Li X, Zhao R, Hou P, Zhao M, Li S, Wang Y, Dai T, Zhou W. 2024. Photosynthetic capacity and assimilate transport of the lower canopy influence maize yield under high planting density. Plant Physiology, 195, 2652–2667.

Yang D, Yang H, Liu D, Wang X. 2024. Research on automatic 3D reconstruction of plant phenotype based on Multi-View images. Computers and Electronics in Agriculture, 220, 108866.

Zheng B, Shi L, Ma Y, Deng Q, Li B, Guo Y. 2008. Comparison of architecture among different cultivars of hybrid rice using a spatial light model based on 3-D digitising. Functional Plant Biology, 35, 900.

Zhou Y, Kusmec A, Schnable P S. 2024. Genetic regulation of self-organizing azimuthal canopy orientations and their impacts on light interception in maize. The Plant Cell, 36, 1600–1621.

Zhu B, Liu F, Xie Z, Guo Y, Li B, Ma Y. 2020. Quantification of light interception within image-based 3-D reconstruction of sole and intercropped canopies over the entire growth season. Annals of Botany, 126, 701–712.

[1]	Chunxiang Li, Yongfeng Song, Yong Zhu, Mengna Cao, Xiao Han, Jinsheng Fan, Zhichao Lü, Yan Xu, Yu Zhou, Xing Zeng, Lin Zhang, Ling Dong, Dequan Sun, Zhenhua Wang, Hong Di. *GWAS analysis reveals candidate genes associated with density tolerance (ear leaf structure) in maize (Zea* mays L.)**[J]. >Journal of Integrative Agriculture, 2025, 24(6): 2046-2062.
[2]	Lihua Xie, Lingling Li, Junhong Xie, Jinbin Wang, Zechariah Effah, Setor Kwami Fudjoe, Muhammad Zahid Mumtaz. A suitable organic fertilizer substitution ratio stabilizes rainfed maize yields and reduces gaseous nitrogen loss in the Loess Plateau, China[J]. >Journal of Integrative Agriculture, 2025, 24(6): 2138-2154.
[3]	Huairen Zhang, Tauseef Taj Kiani, Huabang Chen, Juan Liu, Xunji Chen. Genome wide association analysis reveals multiple QTLs controlling root development in maize [J]. >Journal of Integrative Agriculture, 2025, 24(5): 1656-1670.
[4]	Lanjie Zheng, Qianlong Zhang, Huiying Liu, Xiaoqing Wang, Xiangge Zhang, Zhiwei Hu, Shi Li, Li Ji, Manchun Ji, Yong Gu, Jiaheng Yang, Yong Shi, Yubi Huang, Xu Zheng. *Fine mapping and discovery of MIR172e, a candidate gene required for inflorescence development and lower floret abortion in maize ear*[J]. >Journal of Integrative Agriculture, 2025, 24(4): 1372-1389.
[5]	Xiaoxia Guo, Wanmao Liu, Yunshan Yang, Guangzhou Liu, Bo Ming, Ruizhi Xie, Keru Wang, Shaokun Li, Peng Hou. Matching the light and nitrogen distributions in the maize canopy to achieve high yield and high radiation use efficiency[J]. >Journal of Integrative Agriculture, 2025, 24(4): 1424-1435.
[6]	Yang Wang, Chunhua Mu, Xiangdong Li, Canxing Duan, Jianjun Wang, Xin Lu, Wangshu Li, Zhennan Xu, Shufeng Sun, Ao Zhang, Zhiqiang Zhou, Shenghui Wen, Zhuanfang Hao, Jienan Han, Jianzhou Qu, Wanli Du, Fenghai Li, Jianfeng Weng. A genome-wide association study and transcriptome analysis reveal the genetic basis for the Southern corn rust resistance in maize[J]. >Journal of Integrative Agriculture, 2025, 24(2): 453-466.
[7]	Xin Dong, Baole Li, Zhenzhen Yan, Ling Guan, Shoubing Huang , Shujun Li, Zhiyun Qi, Ling Tang, Honglin Tian, Zhongjun Fu, Hua Yang. Impacts of high temperature, relative air humidity, and vapor pressure deficit on the seed set of contrasting maize genotypes during flowering[J]. >Journal of Integrative Agriculture, 2024, 23(9): 2955-2969.
[8]	Peng Liu, Langlang Ma, Siyi Jian, Yao He, Guangsheng Yuan, Fei Ge, Zhong Chen, Chaoying Zou, Guangtang Pan, Thomas Lübberstedt, Yaou Shen. Population genomic analysis reveals key genetic variations and the driving force for embryonic callus induction capability in maize[J]. >Journal of Integrative Agriculture, 2024, 23(7): 2178-2195.
[9]	Hui Chen, Hongxing Chen, Song Zhang, Shengxi Chen, Fulang Cen, Quanzhi Zhao, Xiaoyun Huang, Tengbing He, Zhenran Gao. Comparison of CWSI and T_s-T_a-VIs in moisture monitoring of dryland crops (sorghum and maize) based on UAV remote sensing[J]. >Journal of Integrative Agriculture, 2024, 23(7): 2458-2475.
[10]	Jiang Liu, Wenyu Yang. Soybean maize strip intercropping: A solution for maintaining food security in China[J]. >Journal of Integrative Agriculture, 2024, 23(7): 2503-2506.
[11]	Hui Fang, Xiuyi Fu, Hanqiu Ge, Mengxue Jia, Jie Ji, Yizhou Zhao, Zijian Qu, Ziqian Cui, Aixia Zhang, Yuandong Wang, Ping Li, Baohua Wang. Genetic analysis and candidate gene identification of salt tolerancerelated traits in maize[J]. >Journal of Integrative Agriculture, 2024, 23(7): 2196-2210.
[12]	Qilong Song, Jie Zhang, Fangfang Zhang, Yufang Shen, Shanchao Yue, Shiqing Li. Optimized nitrogen application for maximizing yield and minimizing nitrogen loss in film mulching spring maize production on the Loess Plateau, China [J]. >Journal of Integrative Agriculture, 2024, 23(5): 1671-1684.
[13]	Jiangkuan Cui, Haohao Ren, Bo Wang, Fujie Chang, Xuehai Zhang, Haoguang Meng, Shijun Jiang, Jihua Tang. *Hatching and development of maize cyst nematode Heterodera zeae* infecting different plant hosts** [J]. >Journal of Integrative Agriculture, 2024, 23(5): 1593-1603.
[14]	Haiqing Gong, Yue Xiang, Jiechen Wu, Laichao Luo, Xiaohui Chen, Xiaoqiang Jiao, Chen Chen. Integrating phosphorus management and cropping technology for sustainable maize production [J]. >Journal of Integrative Agriculture, 2024, 23(4): 1369-1380.
[15]	Pengcheng , Shuangyi Yin, Yunyun Wang, Tianze Zhu, Xinjie Zhu, Minggang Ji, Wenye Rui, Houmiao Wang Chenwu Xu, Zefeng Yang. Dynamics and genetic regulation of macronutrient concentrations during grain development in maize [J]. >Journal of Integrative Agriculture, 2024, 23(3): 781-794.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Shared

Discussed

Cite this article:

About JIA

Editorial board

For authors