Invasive pests like insects, diseases, and weeds harm U.S. Forests and farms by killing trees, reducing biodiversity, and costing billions yearly

The Department of Agriculture National Institute of Food and Agriculture awarded West Virginia University $728,000 under the Agriculture and Food Research Initiative (CFDA 10.310) on February 15, 2026, to develop early-detection tools for invasive forest pests using drone imagery, satellite data, and machine-learning systems.

The project builds a computer system that integrates aerial and satellite imagery to identify signs of invasive insects, diseases, and weeds before they spread, with the system designed to learn continuously from expert feedback and adapt to new pests and seasonal variations. The work team will conduct drone surveys over forests in Pennsylvania and West Virginia, equipping aircraft with color cameras, thermal sensors, spectral sensors, and 3D scanning capability. Ground teams will conduct on-foot verification of detected pest locations. The detection system will fuse drone close-ups with satellite overviews to highlight pest indicators and enable rapid response protocols.

The project also establishes educational components: new college courses at West Virginia University and SUNY Albany will teach students about drones, artificial intelligence, and forest health through hands-on labs and field trips. Project materials will be repurposed into free online resources for K-12 schools and the public, including videos, guides, and workshops.

The period of performance runs from February 15, 2026, through February 14, 2030, with place of performance in Morgantown, West Virginia.

This project tackles early detection using drones, satellites, and smart computer programs to spot these threats before they spread. The main aim is to build tools for quick pest detection and response in forests. In the short term, IT creates a computer system that combines different data types and learns over time. In the long term, IT helps protect agriculture and forestsnationwide. Three key parts guide the work: gathering aerial images and data, building the smart detection system, and teaching K-12 students and stakeholders. The project teams will fly drones over forests in pennsylvania and west virginia. Drones carry cameras for color photos, thermal sensors, spectral sensors, and 3D scans. Satellites add wide-view images from space. Ground teams check spots on foot to confirm the presence of invasive pests. Then, computers will blend drone close-UPS with satellite overviews to highlight pest signs. The system learns continually and gets feedback from experts, which makes IT adapt to new pests or seasons for early detection and rapid responses to invasive pests. From this project, new college classes at west virginia university and suny albany teach students about drones, AI, and forest health through hands-on labs and field trips. Materials turn into free online resources for schools and the public, including videos, guides, and workshops.