Speaker Kaouter Essakkat - Spatial-Dynamic Adoption of AI Weeding Robots: Insights from A Choice Experiment and an Agent-Based Model
- Event Type
- Seminar/Symposium
- Sponsor
- pERE (Program in Environmental and Resource Economics)
- Location
- 428 Mumford Hall
- Virtual
- Join online
- Date
- Nov 17, 2025 12:00 - 1:00 pm
- Speaker
- Kaouter Essakkat, Postdoc, Dept ACE, UIUC
- Views
- 29
- Originating Calendar
- ACE Seminars
Abstract
Over 60 million acres of farmland in the United States are affected by herbicide-resistant weeds, and no new herbicide modes of action have been discovered in the past three decades. Uncontrolled weeds can reduce yields of Midwestern corn and soybeans by up to 50 percent. Artificial intelligence (AI)-enabled robotic weeders, which are small, semi-autonomous machines that navigate under crop canopies, identify weeds, and mechanically remove them, offer a promising alternative. This study examines the technological, economic, and ecological conditions under which these robots will be adopted and how their diffusion could reduce dependence on herbicides. Using a 2024 discrete choice experiment with 253 producers across 12 Midwestern states, we estimate farmers’ willingness to pay for robot attributes, including weed control effectiveness, operational costs, number of passes, and neighboring field conditions. The econometric results are integrated into an agent-based model that simulates dynamic adoption under evolving weed and resistance pressures. Farmers are willing to pay $177–$240 per acre for each 1 percent increase in robot effectiveness and an additional $25–$37 per acre when herbicide resistance is present. Simulation results show that adoption initially rises with increasing resistance, fluctuates with peer effects and temporary returns to herbicides, and stabilizes at about 70 percent. The findings demonstrate that AI-enabled weeders can reduce reliance on herbicides and slow regional resistance buildup. The results provide developers with insights into the robot features most valued by farmers and inform policymakers about their potential to manage herbicide resistance in a region.