MixINN: Accelerating Plant Breeding by Combining Mixed Models and Deep Learning for Interaction Prediction

TL;DR

MixINN combines mixed models and deep learning to predict genotype-environment interactions, improving crop yield predictions and aiding climate-adapted plant breeding.

Contribution

It introduces a novel approach that isolates interaction labels with mixed models and predicts them with deep neural networks, enhancing breeding accuracy.

Findings

Improved prediction of genotype ranking over current methods.

Achieved 5.8% higher average yield in corn trials.

Identified top 20% most productive genotypes with superior accuracy.

Abstract

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.