Ag Tech Talk Podcast: Smart Seeding and the Future of AI on the Farm
13 August 2026 In this episode of Ag Tech Talk, we explore how artificial intelligence and machine learning are helping transform precision agronomy with Dr. Jasmine Neupane, assistant professor of Agricultural Systems Technology at the University of Missouri. Dr. Neupane’s research focuses on integrating digital agriculture technologies, including sensors, remote sensing, and data analytics, to help farmers and ag retailers make more informed, field-specific decisions that improve productivity, profitability, and sustainability.
Her latest research examines how machine learning can be used to optimize variable rate seeding strategies in corn and soybean production by analyzing complex field-level data, including soil characteristics, topography, weather patterns, and historical yield information. In this conversation, Dr. Neupane explains how AI can serve as another tool in the farmer’s toolbox — helping growers move beyond traditional management approaches and make more precise, data-driven decisions.
Podcast Transcript:
*This is an edited and partial transcript.
AgriBusiness Global: Your research found stronger results in corn compared to soybeans. Why is soybean management often more challenging for AI models, and what does that tell us about the crop?
Dr. Jasmine Neupane: AI models work by identifying patterns in data, but soybeans are a highly adaptable crop that respond differently to changing environmental conditions, including weather, soil properties, and seeding rates. While that adaptability is beneficial from a production standpoint, it can make it more challenging for machine learning models to identify consistent patterns. In comparison, corn showed clearer relationships between seeding rates and yield outcomes, allowing AI models to make stronger predictions. Dr. Neupane noted that soybean management may require larger and more robust datasets to improve AI model accuracy and recommendations.
ABG: Were you surprised by the differences between corn and soybean results?
JN: The differences were somewhat expected, but the level of variation between the two crops was notable. The research showed that seeding rate had a particularly significant impact on corn yield, while soybean performance was influenced more by other field characteristics, such as organic matter and elevation differences. The findings demonstrate the importance of considering each crop’s unique characteristics when developing AI-powered agronomic tools.
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