Harnessing Large Language Models and Stochastic Programming for Optimized Plant Breeding Strategies
DOI:
https://doi.org/10.70393/616a6e73.323632ARK:
https://n2t.net/ark:/40704/AJNS.v2n1a03Disciplines:
Biological SciencesSubjects:
GeneticsReferences:
28Keywords:
Large Language Models, Stochastic Programming, Plant Breeding, Optimization Strategies, Genetic Improvement, Crop Yield Prediction, Predictive Analytics, Decision Support Systems, Agricultural Technology, Data-driven ModelingAbstract
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.
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