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.
| Comments: | 11 pages, 1 figure |
| Subjects: | Machine Learning (cs.LG) |
| MSC classes: | 92-08 |
| ACM classes: | J.3 |
| Cite as: | arXiv:2605.04744 [cs.LG] |
| (or arXiv:2605.04744v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.04744 arXiv-issued DOI via DataCite (pending registration) |
From: Aike Potze [view email]
[v1]
Wed, 6 May 2026 10:48:52 UTC (565 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。