Accessing the Effect of Phyllotaxy and Planting Density on Light Use Efficiency in Field-Grown Maize using 3D Reconstructions

TL;DR

The paper addresses how canopy architecture and planting density affect light use efficiency in field-grown maize under dense planting. It develops an end-to-end framework that fuses LiDAR-based 3D canopy reconstructions with a radiative-transfer model (Helios) to simulate PAR interception across genotypes and canopy configurations. Validation against field PAR data shows strong agreement (R^2 = 0.79) and reveals that canopy orientation, particularly off-row parallel leaves, and row spacing are key determinants of light capture. The framework offers actionable insights for breeding and agronomic strategies to maximize PAR interception and potential yield in dense maize systems, with site-specific row-direction considerations highlighted for regional optimization.

Abstract

High-density planting is a widely adopted strategy to enhance maize productivity, yet it introduces challenges such as increased interplant competition and shading, which can limit light capture and overall yield potential. In response, some maize plants naturally reorient their canopies to optimize light capture, a process known as canopy reorientation. Understanding this adaptive response and its impact on light capture is crucial for maximizing agricultural yield potential. This study introduces an end-to-end framework that integrates realistic 3D reconstructions of field-grown maize with photosynthetically active radiation (PAR) modeling to assess the effects of phyllotaxy and planting density on light interception. In particular, using 3D point clouds derived from field data, virtual fields for a diverse set of maize genotypes were constructed and validated against field PAR measurements. Using this framework, we present detailed analyses of the impact of canopy orientations, plant and row spacings, and planting row directions on PAR interception throughout a typical growing season. Our findings highlight significant variations in light interception efficiency across different planting densities and canopy orientations. By elucidating the relationship between canopy architecture and light capture, this study offers valuable guidance for optimizing maize breeding and cultivation strategies across diverse agricultural settings.

Figures (7)

• Figure 1: Overview of the framework for PAR interception analysis: (1) Point Cloud Extraction: Point clouds of field-grown maize were captured using a LiDAR scanner. (2) Segmentation: Different plant organs were segmented from individual plants. (3) 3D Reconstruction: Segmented organs were reconstructed separately to create accurate 3D plant models. (4) Solar Radiation Simulation: Plants of each genotype were replicated to form a virtual field, and solar radiation was simulated using Helios software. (5) PAR Interception Analysis: PAR interception was analyzed under varying planting densities, row directions, and leaf azimuth arrangements.
• Figure 2: Detailed breakdown of the pipeline for 3D canopy modeling and PAR analysis, including (A) data collection of plant materials and PAR data from the field, (B) data processing, (C) framework validation with field PAR measurements, and (D) analyzing PAR for different scenarios.
• Figure 3: Validation of framework accuracy: Comparison of simulated PAR interception from a virtual light meter with field measurements of PAR interception recorded at the same date and time.
• Figure 4: Impact of different leaf azimuth orientations on Photosynthetically Active Radiation (PAR) interception throughout the growing season in Ames, IA. Comparison of PAR interception among three orientations: on-row parallel, off-row parallel, and random.
• Figure 5: Effect of varying row spacing (36 inches, 30 inches, and 20 inches) on PAR interception across different leaf azimuth orientations (on-row parallel, off-row parallel, and random) throughout the growing season in maize canopies.