Using machine learning to create agronomic models for deeper ag insights

Build-Your-Own Ag Model:
Solving Your Toughest Agronomic
Problems with Machine Learning

Flush with data, the agriculture industry faces another challenge: How do you use this data to tell a story throughout the season? Each data point can provide insight on its own; but when leveraging machine learning, these variables can create a bigger picture about what’s happening within the plot or field.

Join our webinar to learn how agronomic modeling takes it one step further by adding data specific to your plots or fields; helping to transform the story from broad assumptions to advanced analytics for insight into performance.

From evaluating the past to informing the present and predicting the future, building an ag model can help solve some of the most complex agronomic problems facing researchers and product developers today. Whether it’s looking at nutrient uptake, understanding crop health throughout the season, or even optimizing yield and outcomes, an agronomic model provides a layer of insight that otherwise isn’t available.

Watch Now

In this 45-minute webinar, Sentera’s Principal Scientist Tyler Nigon will share:

From data collection to decisions, how to use ag modeling for actionable insights

The machine learning lifecycle and what that means to ag modeling development

Real-world uses cases and best practices for agronomic modeling

Meet Our Speaker

Tyler Nigon
Tyler Nigon

Principal Scientist