By Grace Vincent, 2023-2026 FFAR Fellow
at North Carolina State University
In a world where interdisciplinary research is increasingly necessary to solve complex challenges, the ability to step outside one’s field has become essential. Nowhere is this more evident than in efforts to ensure global food security, where plant science, engineering and artificial intelligence must converge to help growers combat crop diseases and improve yields.
Traditional disease identification methods rely on manual surveying, which is time-consuming, labor-intensive and prone to human error. Meanwhile, global crop losses due to biotic stressors amount to an estimated $60 billion annually, making timely mitigation efforts crucial to reducing yield loss. As agricultural challenges grow in complexity, there is a continued need for data-driven solutions to provide faster, more accurate disease recognition.