Towards smart canopies: Algorithmic design of maize canopy architectures that maximize light use efficiency
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| Format: | Preprint |
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2025
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| _version_ | 1866909957448794112 |
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| author | Saleem, Nasla Jubery, Talukder Zaki Zhou, Yan Li, Yawei Krishnamurthy, Adarsh Schnable, Patrick S. Ganapathysubramanian, Baskar |
| author_facet | Saleem, Nasla Jubery, Talukder Zaki Zhou, Yan Li, Yawei Krishnamurthy, Adarsh Schnable, Patrick S. Ganapathysubramanian, Baskar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06064 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards smart canopies: Algorithmic design of maize canopy architectures that maximize light use efficiency Saleem, Nasla Jubery, Talukder Zaki Zhou, Yan Li, Yawei Krishnamurthy, Adarsh Schnable, Patrick S. Ganapathysubramanian, Baskar Quantitative Methods Populations and Evolution 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. |
| title | Towards smart canopies: Algorithmic design of maize canopy architectures that maximize light use efficiency |
| topic | Quantitative Methods Populations and Evolution |
| url | https://arxiv.org/abs/2512.06064 |