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

Yuqun Zhou; Zuen Cen

doi:10.70393/616a6e73.323632

OPEN ACCESS |Research Article ||14 January 2025

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

Yuqun Zhou

University of Wisconsin-Madison

yzhou364@wisc.edu

University of Wisconsin-Madison, USA.

Zuen Cen

Northern Arizona University

zc366@nau.edu

Computer Information Technology, Northern Arizona University, Flagstaff, AZ, USA.

* Corresponding Author¹: Yuqun Zhou, E-Mail: yzhou364@wisc.edu

Publication

Accepted 2025 January 7 ; Published 2025 January 14

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

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 Models , Stochastic Programming , Plant Breeding , Optimization Strategies , Genetic Improvement , Crop Yield Prediction , Predictive Analytics , Decision Support Systems , Agricultural Technology , Data-driven Modeling .

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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