Comparing a chromameter and a hand held NDVI meter to predict nitrogen and water content of turfgrass

Yıl 2020, , 57 - 64, 06.04.2020

Muharrem Keskin , Cagatay Cam Yunus Emre Sekerli

https://doi.org/10.37908/mkutbd.646974

Cited By: 1

Öz

Aims:
Nitrogen content (NC) and water content
(WC) of turfgrass is traditionally determined by laboratory analysis which is
time-consuming, tiresome, laborious and costly. The aim of this study was to
examine the suitability of two hand held optical instruments (GreenSeeker NDVI
meter and chromameter) to evaluate NC and WC of turfgrass.

Methods and Results:
Six turfgrass plots of 1 m x 1 m with a
mixture of five different species were used and variable rate nitrogen
fertilizer (N₀: 0 g N m^-2, N₁: 2.5 g N m^-2,
N₂: 5 g N m^-2) was applied. NDVI measurements were taken
at around noon with a GreenSeeker NDVI instrument from the plots. After mowing,
the color values of the clippings were measured using a hand-held chromameter.
The data were analyzed using correlation and partial least square regression
(PLSR). A high correlation was found between leaf NC, WC, NDVI and color
values. The leaf NC (%) can be estimated from the NDVI (R²val=0.73,
SEP=0.19%) and color values (L*a*b*C*h°) (R²val=0.76; SEP=0.18%).
Also, it was found that the WC (%) can be predicted from the NDVI (R²val=0.40,
SEP=5.07%) and color values (L*C*h°) (R²val=0.69; SEP=3.67%) with
slightly lower accuracy.

Conclusions:
Turfgrass leaf NC can be estimated with
either an NDVI instrument (R²=0.73, SEP=0.19%) or a chromameter (R²=0.76,
0.18%) with reasonable accuracy in a more objective and economical way.

Significance and Impact of
the Study: Considering
the reduction in time and cost required in the NC and WC analysis, we think
that results of this study may be useful for turf field managers. Also,
nitrogen determination with sensors will be a more eco-friendly way if used by managers.

Anahtar Kelimeler

Turfgrass, remote sensing, nitrogen content, water content, NDVI meter, chromameter

Teşekkür

The authors would like to thank Dr. Berkant ODEMIS and Dr. Yurtsever SOYSAL for their support. Also they thank Aysel ARSLAN and Mustafa AKKAMIS for their assistance.

Çim bitkisinin azot ve su içeriği tahmini için Renk ölçer ve NDVI ölçerin karşılaştırılması

Öz

Amaç: Geleneksel olarak çim bitkisinin azot
(Aİ) ve su içeriği (Sİ) tahmini zaman alıcı, yorucu, fazla iş gücü gerektiren
ve masraflı olan kimyasal laboratuvar analizleriyle belirlenmektedir. Bu
çalışmanın amacı, iki farklı el tipi optik algılayıcının (GreenSeeker NDVI
metre ve renk ölçer) çim bitkisinin azot ve su içeriğini değerlendirmedeki
uygunluğunu incelemektir.

Yöntem ve Bulgular: Çalışmada 1 m x 1 m'lik altı adet çim
parselinde değişken düzeyli azotlu gübre uygulaması yapılmıştır. NDVI ölçümleri
arazide el tipi GreenSeeker NDVI ölçer ile gerçekleştirilmiştir. Biçme
işleminden sonra, çim biçkilerinin renk değerleri laboratuvarda renk ölçer
kullanılarak ölçülmüştür. Veriler korelasyon ve kısmi en küçük kareler
regresyon (PLSR) analizi kullanılarak değerlendirilmiştir. Yaprak Aİ, Sİ ile
NDVI ve renk değerleri arasında yüksek korelasyon bulunmuştur. Yaprak Aİ
(%)’nin NDVI (R²val=0.73, SEP=% 0.19) ve renk değerlerinden
(L*a*b*C*h°) (R²val=0.76; SEP=% 0.18) tahmin edilebileceği
tepit edilmiştir. Ayrıca, Sİ (%)’nin NDVI (R²val=0.40,
SEP=% 5.07) ve renk değerlerinden (L*C*h°) (R²val=0.69;
SEP=3.67 %) daha düşük doğruluk ile tahmin edilebileceği belirlenmiştir.

Genel Yorum: Sonuç olarak; çim yaprağı Aİ’nin, NDVI
cihazı veya renk ölçer kullanılarak daha objektif ve ekonomik bir şekilde makul
hassasiyet ile tahmin edilebileceği tespit edilmiştir.

Çalışmanın Önemi ve Etkisi: Azot ve su içeriği analiz süresindeki
azalma dikkate alındığında, çalışma sonuçlarının çim saha bakım sorumluları
için faydalı olacağı değerlendirilmiştir. Ayrıca algılayıcılar ile yapılacak azot
içeriği tespitinin çim alan bakım sorumluları tarafından kullanılması halinde
daha çevre dostu bir yöntem olacağı düşünülmektedir.

Anahtar Kelimeler

Çim, uzaktan algılama, azot içeriği, su içeriği, NDVI ölçer, renk ölçer

Kaynakça

• ASABE (2012) Moisture Measurement-Forages. American Society of Agricultural and Biological Engineers, ANSI/ASAE Standarts, St Joseph, MI, S358.3, USA.
• CAST (2019) Reducing the impacts of agricultural nutrients on water quality across a changing landscape. CAST Issue Paper Number 64. Council for Agricultural Science and Technology (CAST). 20 p.
• Caturegli L, Corniglia M, Gaetani M, Grossi N, Magni S, Migliazzi M, Angelini L, Mazzoncini M, Silvestri N, Fontanelli M, Raffaelli M, Peruzzi A, Volterrani M (2016) Unmanned aerial vehicle to estimate nitrogen status of turfgrasses. PLoS ONE 11(6): e0158268.
• EPA (2012) Frequently asked questions about nitrate and drinking water. United States Environmental Protection Agency (EPA). 2 p.
• Esbensen KH (2009) Multivariate Data Analysis In Practice: An Introduction to Multivariate Data Analysis and Experimental Design. 5th edition. CAMO Inc. Corvallis, Oregon/USA.
• Frank JH (2008) Detection of turfgrass stress using ground based remote sensing. MSc Thesis, University of Florida, Florida, US. 96 p.
• Guillard K, Fitzpatrick RJM, Burdett H (2016) Can frequent measurement of normalized difference vegetative index and soil nitrate guide nitrogen fertilization of Kentucky Bluegrass. Crop Sci. 56: 827-836.
• Inguagiato JC, Guillard K (2016) Foliar N concentration and reflectance meters to guide n fertilization for anthracnose management of Annual Bluegrass putting green turf. Crop Sci. 56: 3328-3337.
• Hocaoglu T (2010) Evaluation of planning and design principles of golf courses in the context of landscape architecture: Gloria Golf Resort case. PhD Thesis, Ankara University, Ankara, Turkey. 151 p.
• Jiang Y, Liu H, Cline V (2009) Correlations of leaf relative water content, canopy temperature, and spectral reflectance in Perennial Ryegrass under water deficit conditions. HortSci. 44(2): 459-462.
• Kacar B, Inal A (2010) Bitki Analizleri (Plant Analysis – in Turkish). Nobel Publication Number: 1241, Ankara. pp 171-212.
• Keskin M, Dodd RB, Han YJ, Khalilian A (2004) Assessing nitrogen content of golf course turfgrass clippings using spectral reflectance. Appl. Eng. Agric. 20: 851–860.
• Keskin M, Karanlik S, Gorucu Keskin S, Soysal Y (2013) Utilization of color parameters to estimate moisture content and nutrient levels of peanut leaves. Turk. J. Agric. For. 37: 604-612.
• Keskin M, Sekerli YE, Gunduz K (2016) Relationship between water content and color properties of chlorotic and non-chlorotic detached crop leaves. J. Agric. Fac. Uludag Univ. 30: 319-324.
• Keskin M, Setlek P, Demir S (2017) Use of Color Measurement Systems in Food Science and Agriculture (in Turkish with abstract in English). International Advanced Researches and Engineering Congress, 16-18 November 2017, Osmaniye. pp 2350-2359. (in Turkish with abstract in English).
• Keskin M, Sekerli YE, Gunduz K (2018) Influence of leaf water content on the prediction of nutrient stress in strawberry leaves using chromameter. Int. J. Agric. Biol. 20: 2103-2109.
• Konica Minolta (2007) Colorimetry: How to Measure Color Differences. Konica Minolta Photo Imaging Inc., USA.
• Mangiafico SS, Guillard K (2007) Cool-Season turfgrass color and growth calibrated to leaf nitrogen. Crop Sci. 47: 1217–1224.
• Moss JQ, Bell GE (2010) Indirect Measurement of Creeping Bentgrass N, Chlorophyll, and Color for Precision Golf Green Management. 10th International Conference on Precision Agriculture Proceedings [CD-ROM], Denver, CO.
• Rodriguez IR, Miller GL (2000) Using near-infrared reflectance spectroscopy to schedule nitrogen applications on dwarf-type bermudagrasses. Agron. J. 92: 423–427.
• Turkish Official Gazette (2004) Regulation on Protection of Waters Against Agricultural Nitrate Pollution (in Turkish). 18 February 2004, Sayı: 25377.