Nov 6 - In-Person Iowa AI, ML and Computer Vision Meetup
Details
Join the Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.
Date and Location
Nov 6, 2025
5:30-8:30 PM
Digital Ag Innovation Lab
3800 University Blvd
Suite 1220
Ames, Iowa 50010
AI in Use Across Corteva R&D
Corteva Agriscience is a leading global agricultural solutions company based on deep integration of technology and science. Corteva’s core innovation drivers are proprietary seed products and differentiated crop protection, and has sights set on the next horizon of value from gene editing, biologicals, and advanced decision science.
A central theme of innovation at Corteva is the transformative role of artificial intelligence across the entire R&D pipeline. AI has already delivered an increase in the speed of time to discovery and significantly improved development timelines, and manufacturing productivity. Corteva is expanding its capabilities, from leveraging GenAI for creating regulatory documents to an AI-powered agronomy tool for its sales team.
This focus on innovation underpins a robust pipeline of new seed and crop protection products. The overarching goal is to leverage the latest technological capabilities to accelerate AI-guided discovery and fully embed data-driven solutions from the laboratory to the farm.
About the Speaker
Matt Smalley serves as the Data Science & Data Engineering Leader for R&D at Corteva Agriscience, where he spearheads the integration of advanced analytics, digital technologies, and scientific research to deliver innovative solutions for farmers worldwide. Matt was raised on a crop and livestock farm in northeast Iowa, an experience that instilled in him a deep appreciation for the challenges and opportunities faced by farmers.
Machine Learning Advances for 3D Phenotyping
Artificial intelligence and machine learning are reshaping agricultural research by enabling new approaches to plant phenotyping and precision agriculture. This talk presents recent advances in 3D plant reconstruction using Neural Radiance Fields (NeRFs) and related learning-based methods for generating high-fidelity visualizations of plant growth. These techniques support scalable, real-time analysis of complex plant structures, offering efficient alternatives to traditional, equipment-intensive approaches.
The session will also highlight how immersive Virtual Reality (VR) environments, combined with AI-driven reconstructions, create new opportunities for collaborative research, allowing distributed teams to virtually analyze, monitor, and interact with crops. By integrating machine learning with visualization and interaction technologies, this work advances precision agriculture and lowers barriers to access, providing both researchers and practitioners with flexible, data-driven tools for breeding, monitoring, and decision-making.
About the Speaker
Adarsh Krishnamurthy is a professor in the mechanical engineering department at Iowa State University, where he currently leads the Integrated Design and Engineering Analysis (IDEA) lab. His research interests include computer-aided design (CAD), GPU and parallel algorithms, cyber-enabled manufacturing, biomechanics, patient-specific heart modeling, solid mechanics, computational geometry, and ultrasonic non-destructive testing. He is a fellow of the Plant Science Institute at Iowa State University. He was elected as a fellow of the American Society of Mechanical Engineers (ASME) in 2024.
Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision
Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference?
This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery.
The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains.
About the Speaker
Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.
Role of AI Foundation Models in Cyber-Agricultural Systems
The emergence of multi-modal AI foundation models presents a paradigm shift opportunity for cyber-agricultural systems by enabling the integration of diverse data types such as imagery, text, and time-series signals. This talk will explore the core concepts, recent advancements, and domain-specific challenges in building and applying multi-modal models to agricultural problems. I will focus on a few of our recent success stories, driving progress in this space, with applications ranging from crop monitoring and yield prediction to sustainable crop management. I will also discuss some practical considerations such as data curation, computational requirements, and model evaluation in the context of Ag foundation models.
About the Speaker
Soumik Sarkar is the Director of the Translational AI Center at Iowa State University and the Associate Director of the NSF/USDA-NIFA AI Institute for Resilient Agriculture (AIIRA). He is a Professor of Mechanical Engineering and Computer Science, and his research focuses on developing AI and machine learning algorithms for cyber-physical systems with applications to manufacturing, transportation, and agriculture. He co‐authored more than 310 peer-reviewed publications and is a recipient of several prestigious honors, including the NSF CAREER Award, the AFOSR Young Investigator Program (YIP) Award, and the Presidential Early Career Award for Scientists and Engineers (PECASE).