Missouri's forests, home to at least 730 wildlife species, are facing urgent health issues from invasive species pests and their impacts necessitating effective monitoring methods

The Department of Agriculture National Institute of Food and Agriculture awarded Lincoln University $739,233 on December 15, 2025, under the 1890 Institution Capacity Building Grants program (CFDA 10.216) to develop a forest health early warning system integrating geospatial technologies and artificial intelligence models to monitor invasive species impacts and forest health issues across Missouri's forests.

Lincoln University will develop and train AI models to detect early signs of forest health degradation, create a user-friendly interface for stakeholders, and build a comprehensive geospatial database combining satellite imagery, unmanned aerial vehicle multispectral and lidar data, and ecological information. The recipient will promote educational programming in forestry, geospatial science, and AI through mentoring, scholarships, and student experimental learning opportunities. Lincoln University will host workshops with the Missouri Department of Conservation to disseminate findings and train state stakeholders in using the forest health early warning system.

The AIFARMS Institute at the University of Illinois Urbana-Champaign will enhance Lincoln University's artificial intelligence capabilities. A co-principal investigator with expertise in remote sensing and forest health will be critical to developing and validating the AI models. Brad Graham from the Missouri Department of Conservation will organize workshops to facilitate knowledge transfer.

Performance occurs in Jefferson City, Missouri, with a period of performance from December 15, 2025, through December 14, 2028.

This project aims to improve forest health monitoring by integrating geospatial technologies with advanced AI models to develop a forest health early warning system (fhews). We will combine satellite imagery, UAV-based multispectral and lidar data, ecological information and location-based forest health metrics to create a comprehensive geospatial database. AI models will analyze this data for forest health monitoring. Key objectives of this integrated project include: 1. Developing and training AI models to detect early signs of forest health issues and creating a user-friendly interface for missouri stakeholders. 2. Promoting educational programs at lincoln university (LU) and increasing student involvement in forestry, geospatial science and AI through mentoring scholarships and providing student experimental learning opportunities. 3. Hosting workshops to disseminate findings and train missouri stakeholders in using fhews to facilitate knowledge transfer and practical application. CO-PD with expertise in remote sensing and forest health will be critical in developing and validating AI models. Aifarms institute at uiuc will enhance LU's AI capabilities. Brad graham from the missouri department of conservation will organize workshops to ensure effective knowledge transfer. This project aligns with the CBG program by educating students, strengthening partnerships and enhancing education, research and extension programs at LU. IT supports USDA's focus on building AI and machine learning capacity in food and agricultural sciences.