Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance

Abstract

Integrating innovative technologies into plant breeding is critical to bolster food and nutritional security under biotic and abiotic stresses in changing climates. While breeding efforts have focused primarily on yield and stress tolerance, emerging evidence highlights the need to also prioritize nutritional quality. Advanced molecular breeding approaches have enhanced our ability to develop improved crop varieties and could be substantially informed by the routine integration of crop modeling and remote sensing technologies. This review article discusses the potential of combining crop modeling and sensing with molecular breeding to address the dual challenge of nutritional quality and stress tolerance. We provide overviews of stress response strategies, challenges in breeding for quality traits, and the use of environmental data in genomic prediction. We also describe the status of crop modeling and sensing technologies in grain legumes, rice, and leafy greens, alongside the status of -omics tools in these crops and the use of AI with directed evolution to identify novel resistance genes. We describe the pairwise and three-way integration of AI-enabled sensing and biophysically and empirically constrained crop modeling into breeding to enable prediction of phenotypic and breeding values and dissection of genotype-by-environment-by-management interactions with increasing fidelity, efficiency, and temporal/spatial resolution to inform selection decisions. This article highlights current initiatives and future trends that focus on leveraging these advancements to develop more climate-resilient and nutritionally dense crops, ultimately enhancing the effectiveness of molecular breeding.

Fig. 1

Schematic of how different remote sensing techniques could be applied to quantify plant structural and functional traits. Boxes are color-coded to indicate whether the trait would be measured from thermal (orange), hyperspectral (blue), LiDAR (green), or RGB camera (pink)and[color figure online]

Fig. 2

Schematic for applying AI/ML and directed evolution to plant immune receptor engineering

Fig. 3

Schematic of opportunities to integrate AI-enabled sensing, genomic prediction/selection, and crop modeling workflows, using crop biomass as an example. The five long black arrows indicate use of AI, and the five gray arrows indicate an opportunity for direct data input between workflows

Fig. 4

Schematic for integration of biophysical modeling, AI-enabled sensing, and breeding/genomics in multi-environment field trials, with applications in the identification of suitability zones (for crop species or cultivars thereof) and development of improved cultivars