Quality Assessment of Turfgrasses Using NTEP Method Compared to an Image-Based Scoring System
محورهای موضوعی : مجله گیاهان زینتی
Fatemeh Kazemi
1
(
Associate Professor, Department of Horticulture and Landscape, Ferdowsi University of Mashhad, Iran
)
Mahmmod Reza Golzarian
2
(
Associate Professor, Department of Biosystems Engineering, Ferdowsi University of Mashhad, Iran
)
Fatemeh Nematollahi
3
(
Ph.D Graduate, Department of Horticulture and Landscape, Ferdowsi University of Mashhad, Iran
)
کلید واژه: Lawn, Digital photo assessment, Quality factors, Human evaluation, Greenspace management,
چکیده مقاله :
The current methods of turfgrass evaluations are often based on human-based assessment methods. However, eliminating subjective errors from such evaluations is often impossible. This research compared the accuracy of human-based and digital image processing-based methods for quality assessment of turfgrasses. Four turfgrass plots were evaluated using the two mentioned methods. In the human-based method, 20 evaluators (10 women and 10 men) and in the image-based method, a digital camera with an artificial and controlled light source were used. This experiment for the first time evaluated the two qualitative characteristics of turfgrass texture and weed growth tolerance using a specific image processing-based technique and the common human-based evaluation method. Further, total coverage, color, and living coverage of the turfgrasses were compared with the two methods. The results of the human-based assessment method showed a wider range and higher standard deviations than that in the image processing method, which seems to be due to the differences between the human's evaluators and errors caused by the human mind. The results also emphasized the accuracy and ease of application of the image-processing-based method. This outcome can have applications for developing a mechanized system for turfgrass quality evaluation across the world.
روشهای کنونی ارزیابی چمن اغلب بر پایه روشهای مبتنی بر ارزیابی انسانی هستند. با این حال، حذف خطاهای ذهنی (شخصی) از چنین ارزیابی اغلب غیرممکن است. این تحقیق صحت روشهای مبتنی بر ارزیابی انسان را در مقایسه با روشهای مبتنی بر پردازش تصویر بررسی میکند. چهار کرت چمن با استفاده از دو روش ذکر شده مورد بررسی قرار گرفت. در روش مبتنی بر ارزیابی انسان، 20 ارزیاب (10 زن و 10 مرد) و در روش مبتنی بر تصویر، یک دوربین دیجیتال با منبع نور مصنوعی و کنترل شده استفاده شد. این آزمایش برای اولین بار دو فاکتورکیفی بافت چمن و تحمل به رشد علفهای هرز را با استفاده از یک تکنیک مبتنی بر پردازش تصویر و روش رایج مبتنی بر ارزیابی انسانی بررسی کرد. علاوه بر این، پوششدهی کل چمن، و رنگ و پوشش زنده چمنها در دو روش اندازهگیری مقایسه شدند. نتایج روش ارزیابی مبتنی بر انسان نشاندهنده دامنه وسیعتر و انحرافات استاندارد بالاتر نسبت به روش پردازش تصویر بود که به نظر میرسد به دلیل تفاوتهای بین ارزیابیکنندگان انسانی و خطاهای ناشی از ذهن انسان است. نتایج همچنین بر دقت و سهولت استفاده از روش مبتنی بر پردازش تصویر تأکید کرد. این نتیجه میتواند کاربردهایی برای توسعه یک سیستم مکانیزه برای ارزیابی کیفیت چمن در سراسر جهان داشته باشد.
Abramov, I., Gordon, J., Feldman, O. and Chavarga, A. 2012. Sex and vision II: Color appearance of monochromatic lights. Biology of Sex Differences, 3: 21. https://doi.org/10.1186/2042-6410-3-21.
Allred, S.R. and Olkkonen, M. 2013. The effect of background and illumination on color identification of real, 3D objects. Frontiers in Psychology, 4: 821. http://dx.doi.org/10.3389/fpsyg.2013.00821.
Bell, G.E., Martin, D.L., Koh, K. and Han, H.R. 2009. Comparison of turfgrass visual quality ratings with ratings determined using a handheld optical sensor. Hort Technology, 19 (2): 309-316.
Bianco, S., Cusano, C. and Schettini, R. 2015. Color constancy using CNNs. 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). DOI: 10.1109/CVPRW.2015.7301275
Bueren, S.K., Burkart, A., Hueni, A., Rascher, U., Tuohy, M.P. and Yule, I.J. 2015. Deploying four optical UAV-based sensors over grassland: Challenges and limitations. Biogeosciences, 12: 163-175.
Casadesús, J., Biel, C. and Savé, R. 2005. Turf color measurement with conventional digital cameras. EFITA/WCCA Joint Congress on it in Agriculture, 25-28 July 2005, Vila Real, Portugal, 804-811.
Crusiol, L.G.T., Nanni, M.R., Furnaletto, R.H., Sibaldelli, R.N.R., Cezar, E. and Metz-Henning, L.M. 2019. UAV-based thermal imaging in the assessment of water status of soybean plants. International Journal of Remote Sensing. DOI: https: //doi.org/10.1080/01431161.2019.1673914.
Fu, J., Fry, J. and Huang, B. 2004. Minimum water requirements of four turfgrasses in the transition zone. HortScience, 39 (7): 1740-1744.
Hoyle, J.A., Yelverton, F.H. and Gannon, T.W. 2013. Evaluating multiple rating methods utilized in turfgrass weed science. Weed Technology, 27 (2): 362-368.
Jayasinghe, C., Badenhorst, P., Wang, J., Jacobs, J., Spangenberg, G. and Smith, K. 2019. An object-based image analysis approach to assess persistence of perennial ryegrass (Lolium perenne L.) in pasture breeding. Agronomy, 9 (501): 1-11.
Karcher, D.E. and Richardson, M.D. 2003. Quantifying turfgrass color using digital image analysis. Crop Science, 43: 943- 951.
Karcher, D.E. and Richardson, M.D. 2003. Quantifying turfgrass color using digital image analysis. Crop Science, 43: 943- 951.
Keskin, M., Han, Y.J., Dodd, R.B. and Khalilian, A. 2008. Reflectance-based sensor to predict visual quality ratings of turfgrass plots. Applied Engineering in Agriculture, 24 (6): 855-860.
Krans, J.V. and 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. Applied Turfgrass Science. doi: 10.1094/ATS-2007-1130-01-TT.
Landschoot, P.J. and Mancino, C.F. 2000. A comparison of visual vs. instrumental measurement of color differences in bent grass turf. Hort Science, 35 (5): 914-916.
Leinauer, B., Van Leeuwen, D.M., Serena, M., Schiavon, M. and Sevostianova, E. 2014. Digital image analysis and spectral reflectance to determine turfgrass quality. Agronomy Journal, 106 (5): 1787-1794.
Lock, R., Rademacher, I., Nonn, H. and Kühbauch, W. 2004. Land use systems in grassland dominated regions. Proceedings of the 20th General Meeting of the European Grassland Federation, Luzern, Switzerland, 21-24 June.
Mintenko, A.S., Smith, S.R. and Cattani, D.J. 2002. Turfgrass evaluation of native grasses for the Northern Great Plains Region. Crop Science, 42: 2018–2024.
Morris, K.N. and Shearman, R.C. 2000. The national turfgrass evaluation program: Assessing new and improved turfgrasses. Diversity-Arlington Then Washington, 16(1/2): 19-21.
Nazemi Rafi, Z., Kazemi, F. and Tehranifar, A. 2020. Public preferences toward water-wise landscape design in a summer season. Urban Forestry and Urban Greening, 48. DOI: 10.1016/j.ufug.2019.126563.
Rodriguez, I.R. and Miller, G.L. 2000. Using a chlorophyll meter to determine the chlorophyll concentration, nitrogen concentration, and visual quality of St. Augustine grass. Hort Science, 35(4): 751-754.
Russ, J.C. 2016. The image processing handbook. CRC press.
Santesteban, L.G., Di Gennaro, S.F., Herrero-Langreo, A., Miranda, C., Royo, J.B. and Matese, A. 2017. High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agricultural Water Management, 183 (31): 49-59.
Swanson, W.H. and Cohen, J.M. 2003. Color vision. Ophthalmology Clinics, 16(2): 179-203.
Watkins, E., Fei, S., Gardner, D., Stier, J., Bughrara, S., Li, D., Bigelow, C., Schleicher, L., Horgan, B. and Diesburg K. 2011. Low-input turfgrass species for the North central United States. Applied Turfgrass Science. doi: 10.1094/ATS-2011-0126-02-RS.
Zhang, C., Pinnix, G.D., Zhang, Z., Miller, G.L. and Rufty, T.W. 2017. Evaluation of key methodology for digital image analysis of turfgrass color using open-source software. Crop Science, 57: 550–558.
