AgTech360

In the first episode of AgTech 360’s From Idea to Impact series, host Adrian Percy talks with Pete Nelson, President and Executive Director of AgLaunch, about a different approach to developing agricultural technologies—one that puts growers at the center of the process. Discover how farmers are helping startups identify real-world challenges, shape and test new solutions, and even share in the financial success of the innovations they help bring to market.

Mohan Tavorath shares his perspective on how artificial intelligence and agricultural data can help growers make faster, more informed decisions. He explores what it takes to make AI tools practical and trustworthy on the farm, why human expertise remains essential, and how AI could ultimately become a seamless part of precision agriculture and everyday farm management.

Dr. Debjani Sihi of NC State explores how artificial intelligence is helping scientists make sense of complex agricultural and environmental systems, uncover patterns in vast datasets and improve predictions. She also explains why AI alone isn’t the answer and why scientific knowledge, human intelligence and real-world expertise remain essential to turning these powerful tools into meaningful solutions for agriculture.

Ranveer Chandra, Vice President at Microsoft, shares his perspective on how artificial intelligence is reshaping agriculture. From the importance of high-quality data and digital infrastructure to the role of collaboration and specialized AI models, he explores what it will take to unlock AI's full potential and how these technologies can help solve some of agriculture's biggest challenges.

Dr. Steven Mirsky of USDA's Agricultural Research Service (USDA-ARS) explains how the digital infrastructure behind artificial intelligence is being built today to power the future of agriculture. From computer vision and image repositories to BenchBot and the Digital Agricultural Systems Hub (DASH), he shares how researchers are creating the datasets, tools, and standards needed to train AI systems that can detect pests, diseases, crop stress, and other field conditions. Steven also discusses why collaboration across government, academia, and industry is essential to moving these innovations from research to real-world impact.