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Academic Journal of Natural Science

AJNS

Academic Journal of Natural Science

ISSN 3078-5170 (print) · ISSN 3078-5189 (online) · Quarterly

Open Access

Open Access Research Article CC BY 4.0 Published 14 January 2025

Harnessing Large Language Models and Stochastic Programming for Optimized Plant Breeding Strategies

Yuqun Zhou1, Zuen Cen2.

  1. 1 University of Wisconsin-Madison
  2. 2 Northern Arizona University

* Corresponding author: Yuqun Zhou · yzhou364@wisc.edu

Abstract

The convergence of Generative AI (GenAI) and stochastic programming introduces unprecedented opportunities for optimizing plant breeding strategies under uncertainty. This paper presents a hybrid framework that integrates Large Language Models (LLMs) with stochastic programming to enhance decision-making in crop improvement. LLMs are employed to analyze vast datasets, generate insights on genotype-environment interactions, and simulate breeding scenarios, while stochastic programming optimizes the selection of genotypes for maximum yield and resilience. Case studies demonstrate the effectiveness of this approach in addressing challenges such as climate variability and evolving market demands, offering a transformative solution for sustainable agriculture.

Keywords

Large Language ModelsStochastic ProgrammingPlant BreedingOptimization StrategiesGenetic ImprovementCrop Yield PredictionPredictive AnalyticsDecision Support SystemsAgricultural TechnologyData-driven Modeling

Publication

Accepted 2025 January 7 ; Published 2025 January 14

Academic Journal of Natural Science, 2025, 2(1), 3078-5170

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Metadata

DOI
10.70393/616a6e73.323632
ARK
ark:/40704/AJNS.v2n1a03
Pages
12–17
References
28
Disciplines
Biological Sciences
Subjects
Genetics

Cite This Article

APA Style

Zhou, Y. & Cen, Z. (2025). Harnessing large language models and stochastic programming for optimized plant breeding strategies. Academic Journal of Natural Science, 2(1), 12-17. https://doi.org/10.70393/616a6e73.323632

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

Funding

Not applicable.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Informed Consent Statement

Not applicable.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contributions

Not applicable.

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Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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Academic Journal of Natural Science (AJNS)
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