AI Sharpens US Agronomy Strategy in Precision Farming

AI tools from Deere, Bayer, and FBN are reshaping agronomy, helping US farmers make sharper input decisions based on real field data

4 Feb 2026

Artificial intelligence is beginning to move from experimentation into routine use across US agriculture, reshaping how farmers manage nutrients, timing and field operations.

For much of the past decade, precision farming has focused on collecting data. AI tools are now helping growers act on it. By combining soil tests, weather patterns, yield maps and predictive models, new systems aim to refine input decisions at field level, helping farmers judge what to apply, where and when.

The shift is driven less by automation than by decision support. Growers are looking for clearer guidance on how to allocate fertiliser and other inputs in ways that improve returns and reduce waste.

Deere has expanded its digital platform to link machinery, analytics and variable-rate technology into a single workflow. The company says this allows farmers to make site-specific decisions directly from the tractor cab. Industry analysts say tighter integration reduces the complexity that has slowed adoption of precision tools in the past.

Bayer is also investing in digital agronomy, piloting AI-based platforms that span nutrients, crop protection and planting strategies. The company is testing tools designed to interpret large datasets and convert them into practical advice for growers. While many remain in trial stages, Bayer has signalled that data and analytics will become a core part of its future offering alongside seeds and chemicals.

Independent data platforms are evolving in parallel. Farmers Business Network has expanded its analytics to give growers more visibility into input performance, pricing trends and cost efficiency. This reflects a broader industry move away from stand-alone digital tools toward shared systems that connect agronomic and commercial data.

Adoption remains uneven. Poor rural connectivity, concerns over data ownership and upfront costs continue to limit uptake, particularly among smaller farms. But improving interfaces and clearer economic benefits are helping to ease those barriers.

As AI tools mature, data-led agronomy is becoming more common in US farming. Practices once seen as a competitive advantage are increasingly being treated as standard practice.