Pamela Roberts, PhD
Professor
University of Florida
Immokalee, FL, USA
Keji Li
University of Florida
Wimauma, Florida, United States
William Turechek
California Strawberry Commission
Watsonville, California, United States
Scott Adkins
USDA-ARS-United States Horticultural Research Laboratory
Ft Pierce, Florida, United States
Weiqi Luo, PhD (he/him/his)
Principal Research Scholar
North Carolina State University
Fort Pierce, Florida, United States
H. Charles Mellinger
Glades Crop Care, Inc.
Jupiter, Florida, United States
Hugh Smith
University of Florida
Wimauma, Florida, United States
Chandrasekar S. Kousik
USDA-ARS-U.S. Vegetable Laboratory
Charleston, South Carolina, United States
Felicia Parks
Glades Crop Care, Inc.
Jupiter, Florida, United States
Leon Lucas
Glades Crop Care, Inc.
Jupiter, Florida, United States
David Johnson
Glades Crop Care, Inc.
Jupiter, Florida, United States
Joeseph Montemayor
Glades Crop Care, Inc.
Jupiter, Florida, United States
Ana Toro
Regrow Agriculture (FluroSat), Cicada Innovations
Eveleigh, New South Wales, Australia
John Shriver
Regrow Agriculture (FluroSat), Cicada Innovations
Eveleigh, New South Wales, Australia
Craig Frey
University of Florida
LaBelle, Florida, United States
Clive Bock, PhD
Research Plant Pathologist
USDA-ARS
Ft. Pierce, Florida, United States
Whitefly have a wide host range and transmit vegetable viruses that cause severe yield losses in the southeastern USA. Integrating satellite-based crop identification with disease risk profiling is a potential basis to manage whitefly that transmit these vegetable viruses. Using Sentinel-2 satellite imagery and machine learning techniques we identified crop types during the growing season. Crop types were ground-truthed and whitefly populations and virus incidence were recorded. Specifically, whitefly populations and tomato yellow leaf curl virus (TYLCV) incidence exhibited spatial autocorrelation up to 1750 m throughout the season. From January to April the spatial autocorrelation further extended up to 5000 m, highlighting the importance of timely focused area wide management. Both whitefly populations and TYLCV incidence were strongly influenced by temperature, particularly during the February to May period. Positive correlations for whitefly populations were detected at multiple lag times and window sizes, with the most significant correlations (p < 0.001) observed between 30 to 85 days lag and window sizes from 20 to 50 days. Surrounding vegetable fields significantly influenced whitefly dispersal, with correlations extending up to 9000 m during peak months. The results emphasize the need for monitoring whitefly populations and virus incidence, incorporating effects of temperature and crop distributions, to effectively control whitefly and TYLCV outbreaks via orchestrated management strategies.