Schumacher, Ally1, Thompson,
Addie2,3
1 Department of Plant Biology; Michigan State
University; East Lansing, MI 48824
2 Department of Plant, Soil & Microbial
Sciences; Michigan State University; East Lansing, MI 48824
3 Plant Resilience Institute; Michigan State
University; East Lansing, MI 48824
ORCiD: 0000-0002-2413-1537
Keywords: Tar Spot, Drone, Maize, Disease, Abstract
Title: Phenomic Prediction Models in Maize Tar Spot Disease Detection
Abstract:
Tar spot disease in maize is the result of a fungal pathogen,Phyllachora maydis , and reduces yield up to 40 percent depending
on severity of infection. Traditional disease monitoring techniques
often suffer from limited coverage and time inefficiency. This study
explores the application of drone-based imagery for phenomic prediction
models in maize tar spot onset and severity detection. Using unoccupied
aerial vehicles (UAVs) equipped with high-resolution cameras,
multi-spectral and high-resolution images are captured of maize fields
prior to disease onset and throughout the season. UAV images are
processed and analyzed to extract essential phenotypic features related
to plant health, including color, texture, size, and shape
characteristics. The resulting data are integrated into machine learning
algorithms to develop predictive models for disease detection and
quantification. Tar spot detection and monitoring techniques would help
with field management, minimizing overall yield loss caused by
infection. The utilization of drone-based imagery for maize tar spot
detection represents a significant advancement in precision agriculture,
with the potential to revolutionize the way we monitor and manage crop
health.