Accelerating improvement of rapeseed for climate resilience and nutritional quality-related traits through machine learning-driven predictive modeling

Articles in Press

Document Type : Research Paper

Authors

Department of Plant Production and Genetics, School of Agriculture, Shiraz University, Shiraz, I. R. Iran

Abstract

Traditional breeding approaches, largely focused on single-trait selection, are proving insufficient to address the polygenic nature of drought tolerance. Here, we present an integrated modeling framework that demonstrates how machine learning (ML) can help accelerate rapeseed (Brassica napus L.) breeding by enabling predictive modeling of complex trait networks, integrating multi-dimensional phenotypic and metabolomic data, and dissecting genotype × environment interactions (GEI). A key component is a multiblock integration workflow that combines phenotypic, metabolomic, and FT-NIR data via early, intermediate, and late fusion. We critically analyzed the application of ML algorithms—including Partial Least Squares Regression (PLSR), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN)—across three interconnected breeding domains. Our study demonstrated that ML models achieved high predictive accuracy (R² = 0.79–0.91) for seed yield and quality traits, identified chlorophyll content (SPAD) as the most importantpredictor of yield under optimal and stress conditions, and uncovered non-linear interactions, such as flowering date × plant height interactions, that classical linear models fail to detect. Integrating ML with classical stability analyses and multi-trait selection approaches and the economically weighted selection index (EWSI) provided a unified framework for identifying superior genotypes merging high-yield traits, metabolic quality, and broad environmental adaptability. Our results indicated that ML-enhanced breeding marks a radical transition away from reactive, trait-based selection to predictive and data-intensive breeding improvement, offering a practical and powerful framework for developing climate-resilient cultivars with optimized agronomic and nutritional values.

Graphical Abstract

Accelerating improvement of rapeseed for climate resilience and nutritional quality-related traits through machine learning-driven predictive modeling

Keywords

Main Subjects

References

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Iran Agricultural Research

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