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AI-designed crop populations for sustainable intensification
Ning Luo1, Junhan Wang2, Morten Graversgaard3
1State Key Laboratory of Maize Bio-breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; Centre for Nature-based Climate Solutions, National University of Singapore, Singapore, Singapore.
Abstract:
Meeting food-security targets within environmental limits requires integrating genetic advances with data-driven design and adaptive management of crop populations as engineered systems. Herein, we propose a framework for AI-designed crop populations, in which AI-enabled approaches coordinate above- and belowground architecture with management to improve productivity, resource-use efficiency, and climate resilience. AI can act as an architect by coupling phenomics with process-based crop models to optimize multiobjective population designs and identify locally tailored configurations. It can also act as a regulator by integrating sensing, model-based prediction, and environmental feedback to guide in-season adaptation under climate variability. By linking trait innovation with population-level interactions and adaptive regulation, this framework offers a potentially transferable route for shifting the yield-efficiency frontier for sustainable intensification.
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