Horst et al. (1984) studied visual evaluation methods common to turfgrass science and determined that visual evaluations among 10 human evaluators were inconsistent and could be considered inadequate. Assessing turfgrass features such as color, density, and overall quality by human visual evaluation is subjective and time consuming (Bell et al., 2002a; Trenholm et al., 1999a). In addition, visual evaluators may be distracted by mowing patterns, rating direction, cloudiness, shadows, and turf wetness (Krans and Morris 2007). These distractions sometimes make it difficult for evaluators to accurately discriminate turf responses or genetic variation. Consequently, turfgrass evaluators require training and experience to attain satisfactory proficiency in turfgrass quality evaluation.
Recently, optical sensing techniques have been introduced that measure the reflectance from turf canopies to determine turfgrass growth (Bell et al., 2004), wear tolerance (Trenholm et al., 1999a, 1999b), herbicide tolerance (Bell et al., 2000), and N fertility (Bell et al., 2002b, 2004; Trenholm et al., 2001). Bell et al. (2002a) demonstrated that optical sensing was not only effective for estimating turf color and percentage of living cover, but was equally efficient and more consistent for that purpose than visual evaluation of tall fescue (Festuca arundinacea) and creeping bentgrass (Agrostis stolonifera). The study also demonstrated that optical sensing results were not influenced by texture and that a combination of turfgrass color and turfgrass cover accounted for most of the variability in models used to predict sensor measurements from human evaluations. These results were further confirmed by Kenworthy et al. (2006) on bermudagrass. Bell et al. (2002a) reported that human evaluations of color or cover alone were not as closely related to sensor measurements as color and cover in combination.
In a former test of optical instruments, it was found that optical sensing required slightly more time and was more costly compared with human visual rating (Bell et al., 2002b). However, improvements in the processing speed of reflectance data instrumentation have made faster collection speeds attainable. At least one modern instrument, the GreenSeeker handheld sensor (NTech, Ukiah, CA) is faster to operate, less expensive (about $3500 in 2006), and easier to use than the instruments tested earlier (Bell et al., 2002a; Trenholm et al., 1999a). The objectives of this study were to assess a handheld optical sensor for evaluating overall turfgrass quality in three turf species over two growing seasons, and to compare the time required of visual evaluation and data entry with the time required for the same functions using a handheld optical sensor.
Bell, G.E. & Xiong, X. 2007 The history, role, and potential of optical sensing for practical turf management 641 658 Pessarakli M. Handbook of turfgrass management and physiology CRC Press Boca Raton, FL
Bell, G.E., Howell, B.M., Johnson, G.V., Raun, W.R., Solie, J.B. & Stone, M.L. 2004 Optical sensing of turfgrass chlorophyll content and tissue nitrogen HortScience 39 1130 1132
Bell, G.E., Martin, D.L., Kuzmic, R.M., Stone, M.L. & Solie, J.B. 2000 Herbicide tolerance of two cold-resistant bermudagrass cultivars determined by visual assessment and vehicle-mounted optical sensing Weed Technol. 14 635 641
Bell, G.E., Martin, D.L., Wiese, S.G., Dobson, D.D., Smith, M.W., Stone, M.L. & Solie, J.B. 2002a Vehicle-mounted optical sensing: An objective means for evaluating turfgrass quality Crop Sci. 42 197 201
Bell, G.E., Martin, D.L., Stone, M.L., Solie, J.B. & Johnson, G.V. 2002b Turf area mapping using vehicle-mounted optical sensors Crop Sci. 42 648 651
Kenworthy, K.E., Taliaferro, C.M., Carver, B., Anderson, J.A., Martin, D.L. & Bell, G.E. 2006 Genetic variation in Cynodon transvaalensis Burtt-Davy Crop Sci. 46 2376 2381
Krans, J.V. & Morris, K. 2007 Determining a profile of protocols and standards used in the visual field assessment of turfgrasses: A survey of National Turfgrass Evaluation Program-sponsored university scientists Online. Appl. Turfgrass Sci.
Park, D.M., Cisar, J.L., McDermitt, D.K., Williams, K.E., Haydu, J.J. & Miller, W.P. 2005 Using red and infrared reflectance and visual observation to monitor turf quality and water stress in surfactant-treated bermudagrass under reduced irrigation Intl. Turfgrass Soc. Res. J. 10 115 120
Raikes, C. & Burpee, L.L. 1998 Use of multispectral radiometry for assessment of rhizoctonia blight in creeping bentgrass Phytopathology 88 446 451
Trenholm, L.E., Carrow, R.N. & Duncan, R.R. 1999a Relationship of multispectral radiometry data to qualitative data in turfgrass research Crop Sci. 39 763 769
Trenholm, L.E., Carrow, R.N. & Duncan, R.R. 2001 Wear tolerance, growth, and quality of seashore paspalum in response to nitrogen and potassium HortScience 36 780 783
Trenholm, L.E., Duncan, R.R. & Carrow, R.N. 1999b Wear tolerance, shoot performance, and spectral reflectance of seashore paspalum and bermudagrass Crop Sci. 39 1147 1152