Meta-Ensemble Learning for Multi-Trait Optimization in Maize Breeding: Combining Gradient Boosting, Random Forests, and Deep Learning with SVM Integration

Dupuy Rony Charles, Pascal Pultrini, Andrea Tettamanzi

Abstract: Plant breeding aims to enhance traits such as yield, drought tolerance, and disease resistance. Traditional Multi-Trait Selection Indices (MTSI) struggle with high-dimensional genomic data and complex trait interactions. We present a meta-ensemble machine learning framework integrating Gradient Boosting, Random Forest, and Deep Neural Networks (DNNs) with a Support Vector Machine (SVM) meta-model to address these challenges. This meta-ensemble approach leverages the strengths of multiple algorithms for improved predictive accuracy and robustness. Experiments on maize datasets show that our meta-ensemble significantly outperforms traditional MTSI methods and individual machine learning models. The meta-ensemble achieves superior predictive accuracy and operational efficiency, with a marked reduction in mean squared error (MSE) and consistent performance across validation sets. This study advances meta-ensemble machine learning in plant breeding, providing a robust framework for multi-

External IDs:dblp:conf/icaart/CharlesPT25