Discussion and Future Work
Basing flight times on significant differences in dew volume indicates that scouting for bermudagrass contamination using dew should be performed within 90 MAS. While dew volume differed between ‘Meyer’ zoysiagrass and ‘Tifway’ hybrid bermudagrass for most sampling times between 0 and 90 MAS (Table 1), the difference in visual appearance may also be influenced by the canopy architecture differences between the two turfgrasses (Volterrani et al., 2017). Te lighter appearance of bermudagrass may be partly due to the greater leaf angle from the stem, which allows light reflectance of dew droplets to be more prominently viewed from above.
Flying various sites with different compositions of bermudagrass and zoysiagrass with varying degrees of turfgrass cover revealed several limitations to this approach. Te denser the stand of both turfgrasses, the easier it is to delineate the bermudagrass contamination. Other weeds, especially large crabgrass, retain large volumes of dew and can appear similar to bermudagrass early in the morning. Soil, whether showing through weak turfgrass regrowing after harvest or freshly exposed after a harvest, can have a lighter color, depending on soil type, which can complicate the differentiation between bermudagrass and zoysiagrass based on the premise of bermudagrass appearing lighter and zoysiagrass appearing darker. Light-colored clippings following a mowing event limit the visibility of the growing turf and can negatively impact color-based scouting with aerial imagery. Trees or structures on the eastern side of fields cast shadows during sunrise, and by the time the sun is above the trees, there may be limited dew on the ground, making it a less ideal time to fly. Cloud conditions, limiting the amount of sunlight to reflect off dew droplets, can limit color differences seen in orthomosaics and affect contamination mapping. Optimal conditions for flying align with the conditions when fields would typically be scouted by walking or driving.
In addition to the ideal time to fly, another major consideration is the optimal height to fly because of the influence on image resolution and area covered. Flying higher allows more area to be covered on a single battery charge but results in lower image resolution. In this research, image resolution was between 0.2 and 0.4 inches (0.508 and 1.016 centimeters) per pixel. Within PIX4Dfields, cells classified as bermudagrass or zoysiagrass were 1 to 3 feet (30.48 to 91.44 centimeters). PIX4Dfields can export shapefiles for precision spraying applications, and the precision of the sprayer used for herbicide applications will dictate the required imagery and processing resolution.
PIX4Dfields is an off-the-shelf, easy-to-use solution for producers to create precision spray maps. However, based on the limitations of this workflow, other sensors, such as those with thermal bands, and image processing methodologies, such as other software programs and machine learning models, should be evaluated in the future.
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Although research funding for this project has ended, research is still ongoing. Remote sensing of bermudagrass contamination will be accomplished through aerial imagery captured with red-green-blue cameras on a UAV. Imagery will be geo-referenced to precisely located ground points in order to allow the development of a spray map. Te size of each contamination “patch” will be physically measured and compared to patches identified in Pix4Dfields in order to determine accuracy and optimal procedures. Precision spray application maps will be created and characterized. Te bermudagrass control trial will be replicated and include multiple applications closer to dormancy to determine killing rates that could be used to stay within label guidelines of per-acre active ingredient loads based on the level of contamination identified within a field. Techniques will be compared to hand application in order to compare kinematics and economics of various technologies.
References Booth, J. C., Sullivan, D., Askew, S. A., Kochersberger, K., & McCall, D. S. (2021). Investigating targeted spring dead spot management via aerial mapping and precision-guided fungicide applications. Crop Science, 61(5), 3134-3144.
Hahn, D. S., Roosjen, P., Morales, A., Nijp, J., Beck, L., Velasco Cruz, C., & Leinauer, B. (2021). Detection and quantification of broadleaf weeds in turfgrass using close-range multispectral imagery with pixel-and object-based classification. International Journal of Remote Sensing, 42(21), 8035-8055.
Huber, L. & Gillespie, T. J. (1992). Modeling leaf wetness in relation to plant disease epidemiology. Annual Review of Phytopathology, 30, 553-577.
Johnson, B. J. (1992). Common bermudagrass (Cynodon dactylon) suppression in Zoysia spp. with herbicides. Weed Technology, 6(4), 813-819.
Madeira, A. C., Gillespie, T. J., & Duke, C. L. (2001). Effect of wetness on turfgrass canopy reflectance. Agricultural and Forest Meteorology, 107, 117-130.
McCurdy, J. D., Held, D. W., Gunn, J. M., & Barickman, T. C. (2017). Dew from Warm-Season Turfgrasses as a Possible Route for Pollinator Exposure to Lawn- Applied Imidacloprid. Crop, Forage & Turfgrass Management, 3(1), 1-6.
Volterrani, M., Mineli, A., Gaetani, M., Grossi, N., Magni, S., & Caturegli, L. (2017). Reflectance, absorbance and transmittance spectra of bermudagrass and manilagrass turfgrass canopies. PLoS ONE, 12, e0188080. https;//
doi.org/10.1371/journal.pone.0188080
Yu, J., Schumann, A. W., Sharpe, S. M., Li, X., & Boyd, N. S. (2020). Detection of grassy weeds in bermudagrass with deep convolutional neural networks. Weed Science, 68(5), 545-5522
TPI Turf News September/October 2025
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