Authors:
Dupuy Charles
1
;
2
;
Pascal Pultrini
2
and
Andrea Tettamanzi
1
Affiliations:
1
Université Côte d’Azur, I3S, Inria, Sophia Antipolis, France
;
2
Doriane Research Softare & Consulting, Av. Jean Medecin, Nice, France
Keyword(s):
Plant Breeding, Multi-Trait Selection Indices (MTSI), Meta-Ensemble Machine Learning.
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-
trait selection. Our approach improves trait prediction reliability and sets a new standard in maize breeding, with potential applications in other crop species, enhancing agricultural productivity and resilience.
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