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

Dupuy Charles, Dupuy Charles, Pascal Pultrini, Andrea Tettamanzi

2025

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.

Paper Citation

in Harvard Style

Charles D., Pultrini P. and Tettamanzi A. (2025). Meta-Ensemble Learning for Multi-Trait Optimization in Maize Breeding: Combining Gradient Boosting, Random Forests, and Deep Learning with SVM Integration. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 876-883. DOI: 10.5220/0013191900003890

in Bibtex Style

@conference{icaart25,
author={Dupuy Charles and Pascal Pultrini and Andrea Tettamanzi},
title={Meta-Ensemble Learning for Multi-Trait Optimization in Maize Breeding: Combining Gradient Boosting, Random Forests, and Deep Learning with SVM Integration},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={876-883},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013191900003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Meta-Ensemble Learning for Multi-Trait Optimization in Maize Breeding: Combining Gradient Boosting, Random Forests, and Deep Learning with SVM Integration
SN - 978-989-758-737-5
AU - Charles D.
AU - Pultrini P.
AU - Tettamanzi A.
PY - 2025
SP - 876
EP - 883
DO - 10.5220/0013191900003890
PB - SciTePress