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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.
Trends in Plant Science
|July 28, 2026
Summary
Artificial intelligence (AI) can design advanced crop populations by optimizing traits and management for better food security and environmental sustainability. This AI-driven framework enhances crop productivity, resource efficiency, and climate resilience.
Area of Science:
- Agricultural Science
- Computational Biology
- Systems Biology
Background:
- Meeting global food security targets necessitates sustainable agricultural intensification within environmental limits.
- Integrating genetic advancements with data-driven crop population management is crucial for achieving these goals.
Purpose of the Study:
- To propose a framework for AI-designed crop populations that optimizes productivity, resource-use efficiency, and climate resilience.
- To leverage AI for coordinating crop architecture and management strategies.
Main Methods:
- Coupling high-throughput phenomics with process-based crop models for multiobjective population design.
- Utilizing AI as an architect to optimize population configurations for local environments.
- Employing AI as a regulator for in-season adaptive management based on sensing and prediction.
Main Results:
- AI can optimize crop population designs by integrating above- and belowground traits with management strategies.
- AI-driven adaptive regulation enables in-season adjustments to climate variability.
- The framework links trait innovation with population dynamics for enhanced performance.
Conclusions:
- This AI-enabled framework offers a transferable approach for advancing sustainable intensification.
- It shifts the yield-efficiency frontier by optimizing crop populations as engineered systems.
- The approach enhances agricultural productivity and resilience in the face of environmental challenges.
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