Remote Detection and Precision Control of Bermudagrass within Zoysiagrass Sod Production Fields


Researchers: Amy Wilber and James McCurdy, Mississippi State University


Introduction


Bermudagrasses (Cynodon species) and zoysiagrasses (Zoysia species) are desirable turfgrasses vegetatively propagated as sod. Pure stands of a single species are desired, but bermudagrass easily contaminates zoysiagrass fields via vegetative and seeded propagation. Bermudagrass is competitive, and once established, can be hard to eradicate ( Johnson, 1992).


Bermudagrass is visually distinct from zoysiagrass, especially when covered in early morning dew/guttation. However, within large zoysiagrass stands, identification of bermudagrass from aerial imagery may be a faster and more precise alternative to visual identification, thus permitting precision spray application for its control. Te varied canopy architecture of zoysiagrass and bermudagrass impacts the spectral reflectance and absorbance of the two species (Volterrani et al., 2017) and may be beneficial in identification using a multispectral sensor.


In addition to varied canopy architecture, dew retention may be another identifying feature between species that can be evaluated using a camera or multispectral sensor mounted on an unmanned aerial vehicle (UAV). Dew forms on plant leaves when the leaf surface temperature falls below the dewpoint of the air (Huber & Gillespie, 1992). Te accumulation of dew is greatly influenced by the spatial characteristics of leaves, such as their size, height, inclination, and azimuth, as well as the structure of the canopy, including factors like leaf area and canopy height (Huber & Gillespie, 1992). Madeira et al. (2001) have found that reflectance values are affected by species, surface wetness, and sun elevation. As sun elevation increases, dew volume decreases and reflectance values increase (Madeira et al., 2001).


Data collected from UAVs can be used to create maps for precision-guided pesticide applications. Booth et al. (2021) have mapped spring dead spot infestations on golf course fairways from aerial imagery to create a site-specific fungicide treatment plan to make targeted applications and reduce overall fungicide usage. In turfgrass systems, broadleaf (Hahn et al., 2021) and grassy (Yu et al., 2020) weeds have been identified using machine learning techniques for potential use in spot spraying.


Research conducted at Mississippi State University, and supported by Te Lawn Institute, aims to quantify dew deposition and retention and to identify patches


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Figure 2. A 1 square foot (0.0929 square meter) frame used to measure dew collection area.


TPI Turf News September/October 2025


of bermudagrass within zoysiagrass with aerial imagery, with the goal of creating precision spray maps for spot applications of herbicide treatments. A trial was conducted to evaluate various herbicides at increasing rates to determine if a single application at an increased dose can control bermudagrass.


Methods


Dew Quantification Dew on mature ‘Meyer’ zoysiagrass and ‘Tifway’ bermudagrass stands maintained at 1.25 inches (3.175 centimeters) was quantified on 15, 18, 23, and 24 August 2024. Collection kits containing a pair of nitrile gloves, five paper towels, and a resealable plastic bag were assembled and weighed prior to collection (Figure 1). At each time point, dew was collected from a 1 square foot (0.0929 square meter) area of grass until moisture-free (Figure 2). Te collection kits were weighed after collection to determine the increase in mass and the equivalent volume of fluid. Dew was collected from adjacent surfaces every 15 minutes from sunrise to 180 minutes after sunrise to determine the duration of dew presence in the morning. Dew volumes were subjected to an analysis of variance and pairwise comparisons in R Studio.


Figure 1. Collection kit containing an airtight bag, gloves, and paper towels.


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