Optimizing Bermudagrass Management Strategies Using Aerial Imagery and Wireless Capacitive Soil Sensors
| dc.contributor.author | Roberson, Travis Leon | en |
| dc.contributor.committeechair | McCall, David Scott | en |
| dc.contributor.committeemember | Sandor, Daniel Shankar | en |
| dc.contributor.committeemember | Straw, Chase | en |
| dc.contributor.committeemember | Shafian, Sanaz | en |
| dc.contributor.department | Plant Pathology, Physiology and Weed Science | en |
| dc.date.accessioned | 2025-03-29T08:01:16Z | en |
| dc.date.available | 2025-03-29T08:01:16Z | en |
| dc.date.issued | 2025-03-28 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | Hybrid bermudagrass is extensively used in the transition zone due to its excellent drought and wear tolerance. Recent research has aimed to refine hybrid bermudagrass management through incorporating technologies to help make management more efficient and practical. From 2021 to 2024, studies explored the use of drone vehicles and wireless soil sensors to enhance irrigation practices and mitigate winter stress. The first study examined the influence of surface moisture on leaves of creeping bentgrass and hybrid bermudagrass species under green and fairway mowing heights, in conjunction, with two different flight times of 9:00 and 14:00 to induce differing sun incidence angles to explore how all these factors may impact visual drone pixel data. The research found a positive correlation between green and red pixel data when compared to soil water content for all factors but was not similar for pixel data involving blue light. Furthermore, little influence was observed with natural dew deposits or light irrigation on the turfgrass surface when evaluated by green and red pixel data for all locations and flight times. This suggests that drone flights are not impacted by these factors under consistent environmental conditions and field drone flight times can be expanded beyond solar noon timeframes. The second study evaluated the use of aerial thermal imagery to assess irrigation uniformity on golf courses. Thermal imagery to be used as a metric for thermal canopy temperature data and was collected for pre and post irrigation applications compared to catch can volume data. Our data indicates a stronger correlation with ultradwarf bermudagrass greens than fairway height management zones. Key findings indicate thermal imagery can be used to identify moisture stress areas caused by limitations in irrigation system design or delivery for ultradwarf bermudagrass greens more confidently than fairway height of cut areas. The data is likely more related to the influence of physical soil properties within these tested locations and how water infiltration occurs in relation to irrigation applications. Identifying these irrigation limitations with drone data potentially allows for identifying irrigation system operational weaknesses and overall improvement in water use efficiency. The final study focused on using wireless soil sensors to monitor soil conditions during winter, aiding in the prevention of winterkill. Wireless soil sensors provided efficient soil temperature and moisture data delivery during extreme weather events as recorded during covering and uncovering for winter covering treatments of no cover, single cover or double covers for extreme weather events leading to putting green cover protection. Collectively, these studies underscore the potential of advanced technologies to improve turfgrass management, the health and playability of intensively managed turf, and to and promote water conservation on golf courses. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:42544 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/125114 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Precision | en |
| dc.subject | Turfgrass | en |
| dc.subject | Remote | en |
| dc.subject | Aerial | en |
| dc.subject | Sensor | en |
| dc.subject | Technology | en |
| dc.subject | Bermuda | en |
| dc.title | Optimizing Bermudagrass Management Strategies Using Aerial Imagery and Wireless Capacitive Soil Sensors | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Plant Pathology, Physiology and Weed Science | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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