Towards smart canopies: Algorithmic design of maize canopy architectures that maximize light use efficiency

TL;DR

This paper introduces a computational framework combining plant modeling and evolutionary algorithms to optimize maize canopy architecture, resulting in designs that significantly improve light interception and could inform breeding strategies.

Contribution

It presents a novel integrated approach to design maize canopies with optimized architectures for light use efficiency, supported by simulations and biological plausibility.

Findings

Optimized maize architecture increases light interception compared to traditional hybrids.

Emergent traits include vertical stratification and azimuthal tiling to reduce shading.

Designs are applicable across different latitudes and planting densities.

Abstract

We present a computational framework that integrates functional-structural plant modeling (FSPM) with an evolutionary algorithm to optimize three-dimensional maize canopy architecture for enhanced light interception under high-density planting. The optimization revealed an emergent ideotype characterized by two distinct strategies: a vertically stratified leaf profile (steep, narrow upper leaves for penetration; broad, horizontal lower leaves for capture) and a radially tiled azimuthal arrangement that breaks the conventional distichous symmetry of maize to minimize self and mutual shading. Reverse ray-tracing simulations show that this architecture intercepts significantly more photosynthetically active radiation (PAR) than virtual canopies parameterized from high-performing field hybrids, with gains that generalize across multiple U.S. latitudes and planting densities. The optimized trait combinations align with characteristics of modern density-tolerant cultivars, supporting biological plausibility. Because recent gene editing advances enable more independent control of architectural traits, the designs identified here are increasingly feasible. By uncovering effective, non-intuitive trait configurations, our approach provides a scalable, predictive tool to guide breeding targets, improve light-use efficiency, and ultimately support sustainable yield gains.