Emily Bick, assistant professor of entomlogy, received USDA-NIFA funding for her project Eavesdropping on insects: Establishing economic thresholds for agricultural decisions through the Data Science for Food and Agricultural Systems (DSFAS) program. It was among 14 projects sharing $7.4 million in funding.

Project summary (from CRIS website): Entomologists have used microphones since 1901, but these tools are challenging for farmers and scientists due to complexity in identifying diverse insect species. Applying machine learning to audio sensors, though rare in entomology, shows promise. Specifically, using contact microphones with tailored algorithms offers a cost-effective way to monitor economically important insect stages, as demonstrated by this team (Mehrotra et al., 2023). Practitioners at the farmer, regional government, commodity board, and industry levels independently identified the barrier to the use of the Insect Eavesdropper in decision-making as the link to economic thresholds. Therefore, we propose to (1) develop an algorithm correlating Insect Eavesdropper signals to pest density and (2) develop a decision tree framework to interpret the algorithm output as an economic threshold. We will evaluate the algorithm and framework for leaf defoliating, sap-feeding, root feeding, and seed feeding insect pests on three major commodities. If successful, this project will result in a new type of economic threshold based on autonomous sensing of insect damage to crops. By correlating the sensor signals with pest density and crop damage, we aim to empower agricultural stakeholders to make informed decisions.This research involves using a device called the Insect Eavesdropper to listen to the sounds insects make when they feed on crops. By analyzing these sounds, the researchers hope to estimate the number of insects and the damage they’re causing. The study covers different types of insects and crops. For example, they’re looking at beetles on soybeans and potatoes, and corn earworms on corn. They’re also studying how insects feed at different times of day. The researchers are also trying to understand the relationship between the number of insects and the damage they cause. For instance, they’re studying how much leaf damage beetles cause on soybeans, and how many aphids are on soybean and sorghum plants. In addition, they’re looking at how corn rootworms affect corn roots and how corn earworms damage corn kernels. They’re using the Insect Eavesdropper to continuously monitor these insects and record their feeding sounds. The researchers are developing algorithms to interpret the sounds recorded by the Insect Eavesdropper. These algorithms will help them understand the relationship between the number of sounds (or ‘events’) and the amount of damage caused by the insects. Finally, they’re creating a decision tree, a type of model that can help make decisions based on the data. This decision tree will classify the damage as ‘below’, ‘at’, or ‘above’ the economic threshold – the point at which it becomes cost-effective to take action against the insects. This could help farmers make better decisions about managing pests on their crops. This project has the potential to revolutionize pest monitoring in agriculture, significantly reducing costs and labor while enhancing the efficacy and sustainability of pest management strategies, ultimately bolstering global food security, and mitigating the impact of herbivorous insects on agricultural crops.