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The paper introduces MixINN, a hybrid approach combining mixed models and deep neural networks to predict genotype-environment interactions in plant breeding. Mixed models first extract high-quality interaction labels from multi-environment trial data, which are then used to train a deep neural network to predict the performance of new crop varieties under future environmental conditions. Experiments on corn yield prediction across the US show that MixINN outperforms existing plant breeding methods, increasing average yield by up to 7.2% when targeting specific environments.
A hybrid AI model can boost corn yield predictions by up to 7.2%, offering a promising path to accelerate climate-adapted crop development.
Plant breeding underpins global food security through incremental, accumulating improvements in crop yield, quality and sustainability, achieved via repeated cycles of crop ranking, selection and crossing. Climate change disrupts this process by altering local growing conditions, thereby shifting the relative performance of crop genotypes. Predicting these relative changes in yield is critical for food security. Yet, this problem remains an open challenge in plant breeding, and relatively unexplored within the AI community. We propose MixINN, an approach that first isolates high-quality genotype-environment interaction labels using mixed models, and then predicts these interactions for new crop varieties in future environmental conditions with a deep neural network. We evaluate our method on a corn multi-environment trial across the continental United States and show improved prediction of genotype ranking over current plant breeding methods. MixINN demonstrated superior performance in identifying the 20% most productive corn genotypes, leading to a 5.8% higher average yield, which further improved to 7.2% when targeting specific growing environments. These are competitive results for real-world breeding programs, demonstrating the potential of AI research in accelerating the development of climate-adapted crops, and improving future food security under climate change.
