Optimizing Bermudagrass Management Strategies Using Aerial Imagery and Wireless Capacitive Soil Sensors

Hybrid bermudagrass (HBG) (Cynodon dactylon (L.) Pers. x transvaalensis Burtt Davy) is one of the most commonly used turfgrasses in the transition zone due to its drought and wear tolerance. Over the years, a combination of history, experience and research has provided best management practices for abiotic stress management of HBG through chemical and cultural field trials. As new tools and technologies to apply to HBG management emerge, research is necessary in order to better understand how these can be implemented in the decision making process for optimal HBG management. As technology rapidly evolves, understanding how to properly implement innovation is vital for outputs to be greater than the inputs for sustainable management. Three studies were conducted between 2021 and 2024 in Virginia to enhance understanding of how small unmanned aerial vehicles and wireless capacitive soil sensors can aide in expediting data collection for actionable decision making related to irrigation practices and winterkill stress mitigation. The first study assessed the impact of morning leaf wetness from dew and subsequent removal on remotely sensed visible imagery for creeping bentgrass and HBG. The data suggests that leaf wetness minimally influences drone-derived green to red ratio index data while maintaining a moderate correlation with soil water content (r² = 0.48. The second study evaluates the effectiveness of aerial thermal imagery in assessing the distribution uniformity of golf course irrigation systems. A modest correlation existed between irrigation applied as measured by catch can volume and thermal mean canopy temperature (Tc) values (r = 0.40). Furthermore, the coefficient of determination between Tc and catch can volume, varied between tee (r2 = 0.19-0.41) and green (r2 = 0.54-0.68) locations, influenced by turfgrass canopy density and soil physical properties. The use of drone-captured thermal imagery shows potential irrigation distribution uniformity through drone thermal imagery to make these evaluation metrics seamless, though techniques need refinement for widescale industry adoption to be applied for potential irrigation management decision making. The final study focuses on utilizing capacitive soil sensors to monitor soil temperature and moisture during winter covering events for ultradwarf bermudagrass (UDB), indicating that wireless sensors can accurately document soil moisture and temperature trends prior, during, and post-covering events. Within the study, the lowest recorded soil temperatures at 33.0◦F for Green 9 and 31.0◦F for Green 1 under the no cover treatment, and no winter injury was observed, suggesting that UDB may be able to tolerate these soil temperatures for brief periods under fully dormant conditions. Lastly, for the coldest covering event on Green 1, soil moisture fluctuated the most within the uncovered treatment compared to single and double covers, likely due to freeze and thaw cycles of the soil water, suggesting that soil moisture levels are a likely contributor to winterkill potential. Collectively, these studies highlight the potential of advanced technologies in enhancing turfgrass management and water conservation efforts in golf course maintenance of hybrid bermudagrass areas.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Plant Pathology, Physiology and Weed Science

Department dc:contributor.department

Plant Pathology, Physiology and Weed Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Roberson, Travis Leon

Chair dc:contributor.committeechair

• McCall, David S.

Committee members dc:contributor.committeemember

• Sandor, Daniel Shankar
• Straw, Chase
• Shafian, Sanaz

• Precision
• Turfgrass
• Remote
• Aerial
• Sensor
• Technology
• Bermuda

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:42544

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/125114