A study conducted by researchers at New York University (NYU) together with their colleagues from National Taiwan University, Purdue University, and the University of Illinois has found genes, through machine learning, that help crops grow with less fertilizer and predict additional traits in plants and disease outcomes in animals.

In the Nature Communications paper, it was indicated that the research team used machine learning, a type of artificial intelligence used to detect patterns in data. As a proof-of-concept, the researchers showed that genes whose responsiveness to nitrogen is evolutionarily conserved between two diverse plant species—Arabidopsis, and varieties of corn—significantly improved the ability of machine learning models to predict genes of importance for how efficiently plants use nitrogen, a crucial nutrient for plants and the main component of fertilizer. Crops that use nitrogen more efficiently grow better and require less fertilizer, which has economic and environmental benefits.

Experiments validated eight master transcription factors as genes of importance to nitrogen use efficiency. They showed that altered gene expression in Arabidopsis or corn could increase plant growth in low nitrogen soils, which they tested both in the lab at NYU and in cornfields at the University of Illinois. The researchers showed that machine learning can be applied to other traits and species by predicting additional traits in plants, including biomass and yield in both Arabidopsis and corn. They also showed that this approach can predict genes of importance to drought resistance in rice, as well as disease outcomes in animals.

For more details, read the news releases from New York University and the University of Illinois.