Leveraging Real-Time Insect Traps and Data Analytics to Improve Corn Earworm Risk Prediction

NC State has been tracking corn earworm abundance in North Carolina for decades. Analysis of this data has shown that high numbers of corn earworm are related to the abundance of soybean in the surrounding landscape. In 2019, NC State funded the development of a real-time pheromone trap targeting corn earworm, deploying 20 traps in soybean fields across five counties. Testing the trap's durability and documenting corn earworm abundance variation revealed several improvements needed for commercialization. This project aims to refine the trap design and develop data analytics to provide grower-accessible corn earworm risk prediction tools.

Sponsor

Principle Investigators

Anders Schmidt Huseth
Alper Yusuf Bozkurt
Natalie Genevieve Nelson

More Details

Corn earworm (Helicoverpa zea Boddie) has been the target of black light and pheromone trapping networks across North Carolina for decades. Analysis of this historical data has shown that high numbers of corn earworm are positively related to the abundance of soybean in the surrounding landscape (Dorman and Huseth in prep). However, we do not know how to leverage this new knowledge into accurate risk predictions for soybean growers. In 2019, the NCSPA funded the development of a real-time pheromone trap targeting corn earworm. Following a period of development and small scale testing, we deployed 20 traps in soybean fields across 5 NC counties. First, we tested the trap durability and identified several improvements that will be needed to move this trap toward commercialization (power usage, weatherizing). Second, we documented a remarkable amount of corn earworm abundance variation in space and time. Here, we propose to refine our trap design and develop predictive data analytics using the near real-time data. Results of this work will provide the foundation for grower accessible corn earworm risk prediction tools.