Bayer uses generative AI and machine learning to speed crop breeding and agronomy

Overview

Bayer uses AI and data science across its agriculture business. Its precision breeding platform, combining machine learning and digital twin technology, has cut a breeding cycle from five to six years down to four months. Bayer trained a large language model called E.L.Y. on internal agronomy data to answer farm management questions, and uses AI models to guide planting density and irrigation decisions on its own seed-production fields, increasing field productivity by over 30%; it also offers AI-powered seed scripts and planting-density recommendations to growers through its FieldView digital farming platform, which manages more than 220 million acres in over 20 countries.

The challenge

Bayer's traditional plant breeding cycle historically took five to six years to complete. Agronomists faced a time-consuming process to get accurate answers to farm management questions, and Bayer sought to reduce the environmental impact of crop protection products and better navigate the volatility and complexity of its crop protection supply chain.

The solution

Bayer's breeders use machine learning and digital twin technology within its precision breeding platform to write new genetic combinations and anticipate a plant's performance across thousands of micro-level climatic and soil conditions. Bayer trained a large language model, E.L.Y., on years of internal agronomy data, trial insights and aggregated agronomist experience to answer agronomy, farm management and product questions in natural language. Bayer also developed CropKey, a process that uses AI and predictive modeling to virtually test crop protection molecules for safety and sustainability. FieldView, Bayer's digital farming platform managing more than 220 million acres in over 20 countries, offers AI-powered seed scripts that use machine learning with satellite imagery or yield data to divide fields into management zones, with predictive models suggesting planting density based on a grower's yield or profit goals. Bayer also uses AI models to guide planting density, timing and irrigation decisions on its own seed production fields, and machine learning to identify crop protection supply chain vulnerabilities.

Reported business value

Bayer's AI-powered precision breeding program completes a breeding cycle in four months versus the historical five to six years, which is expected to more than double Bayer's rate of genetic innovation by 2030. The E.L.Y. pilot is already unlocking productivity for Bayer teams in the United States while significantly outperforming out-of-the-box LLMs currently serving the agricultural market. Using AI models to inform planting and irrigation decisions on Bayer's own production fields has increased field productivity by over 30%.