GAN-Based Crop Genetic Enhancement and Breeding Strategy Generative

This study endeavors to propose a novel Generative Adversarial Networks GANs framework specifically designed for crop breeding, aimed at augmenting crop genetic information and formulating efficient breeding strategies. It addresses the pivotal scientific question of how to enhance the diversity and quality of crop genetic information and develop efficient breeding strategies to elevate the efficacy and success rates of crop breeding initiatives. This work proposes a methodology leveraging Generative Adversarial Networks GANs to enrich crop genetic information and craft effective breeding strategies. Through an in-depth examination of GANs-based methodologies for the enhancement of crop genetic information, this research aims to simulate and generate crop genetic data characterized by elevated genetic diversity, thereby significantly expanding the genetic resource pool and offering a wider array of genetic materials for breeding purposes. Specifically, by simulating rare or inadequately explored genetic variations, GANs hold the potential to unveil novel genetic traits and characteristics, thus opening new avenues for crop enhancement. Moreover, this study will leverage the augmented genetic information to refine breeding strategies through GANs models, encompassing not only the optimization of hybrid combinations but also the prediction of environmental factors and management practices on the expression of crop traits. In essence, this research aspires to provide scientific and precise decision-making support for breeders, markedly enhancing the success rate and efficiency of breeding programs.