밀에서 질소 시비 조건에 따른 생육 단계별 초분광 특성 변화 = Changes in the Hyperspectral Characteristics of Wheat Plants According to N Top-dressing Rates at Various Growth Stages

국문 초록 (Abstract)

적절한 질소 시비는 작물에 초형을 개선하는 한편, 엽록소 유지에도 도움을 주어 엽노화를 억제하고 광합성도 증대시켰다. 드론을 활용해 얻어진 잎의 RGB 값은 4월 29일에서 추비량 증가에 따라 RGB 값의 뚜렷한 차이를 나타내 단순한 엽색 분석도 작물의 생리적 상태 평가에 활용할 수 있음을 보여주었다. 휴대용 측정기를 이용한 실험에서 추비 조건에 따른 NDVI와 SPAD 값은 3월 19일에 큰 차이 확인할 수 없었다. 그러나 초분광카메라를 통한 분석에서 추비량 증대에 따라 780 nm보다 큰 파장대인 NIR 영역에서 반사율 증가가 확인되었다. 이는 시비 효과가 명확히 드러나지 않는 생육 초반에도 초분광카메라 활용해 작물 상태를 진단할 수 있음을 보여준다. 포장에서 추비 수준이 낮을수록 4월 29일에는 가시광선 영역의 반사율이 증가하고, NIR 영역의 감소가 확인되어 시비에 따른 영향을 확인할 수 있었다. 초분광카메라를 이용한 식생지수 확인으로 엽록소 함량, 질소 부족 정도, 광합성 상태 분석에 근거한 시비 효과 평가가 가능하였다.

참고문헌 (Reference)

1 Peñuelas, J., "Visible and near-infrared reflectance techniques for diagnosing plant physiological status" 3 : 151-156, 1998

2 Gitelson, A. A., "Use of a green channel in remote sensing of global vegetation from EOS-MODIS" 58 : 289-298, 1996

3 Wijitdechakul, J., "UAV-based multispectral image analysis system with semantic computing for agricultural health conditions monitoring and real-time management" 459-464, 2016

4 Ivushkin, K., "UAV based soil salinity assessment of cropland" 338 : 502-512, 2018

5 Goetz, A. F. H, "Three decades of hyperspectral remote sensing of the Earth: A personal view" 113 : S5-S16, 2009

6 Li, B., "The estimation of crop emergence in potatoes by UAV RGB imagery" 15 : 15-, 2019

7 Mahlein, A. K., "Spectral signature of sugar beet leaves for the detection and differentiation of diseases" 11 : 413-431, 2010

8 Gitelson, A., "Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. spectral features and relation to chlorophyll estimation" 143 : 286-292, 1994

9 Fritschi, F. B., "Soybean leaf nitrogen, chlorophyll content, and chlorophyll a/b ratio" 45 : 92-98, 2007

10 Peñuelas, J., "Semi-empirical indices to assess carotenoids/chlorophyll-a ratio from leaf spectral reflectance" 31 : 221-230, 1995

11 Carter, G. A, "Responses of leaf spectral reflectance to plant stress" 80 : 239-243, 1993

12 Gitelson, A. A., "Remote sensing of chlorophyll concentration in higher plant leaves" 22 : 689-692, 1998

13 Sims, D. A., "Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages" 81 : 337-354, 2002

14 Gitelson, A. A., "Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves" 160 : 271-282, 2003

15 Blackburn, G. A, "Relationship between spectral reflectance and pigment concentrations in stacks of deciduous broadleaves" 70 : 224-237, 1999

16 Vogelmann, J. E., "Red edge spectral measurements from sugar maple leaves" 14 : 1563-1575, 1993

17 Netto, A. T., "Photosynthetic pigments, nitrogen, chlorophyll a fluorescence and SPAD-502 readings in coffee leaves" 104 : 199-209, 2005

18 Merzlyak, M. N., "Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening" 106 : 135-141, 1999

19 Amanullah, K., "Nitrogen levels and its time of application influence leaf area, height and biomass of maize planted at low and high density" 41 : 761-768, 2009

20 Rouse, J. W., "Monitoring vegetation systems in the Great Plains with ERTS" NASA 1 : 301-317, 1974

21 Feng, W., "Monitoring leaf nitrogen status with hyperspectral reflectance in wheat" 28 : 394-404, 2008

22 Han, L., "Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data" 15 : 10-, 2019

23 Zheng, T., "Mechanisms of wheat (Triticum aestivum) grain storage proteins in response to nitrogen application and its impacts on processing quality" 8 : 11928-, 2018

24 Birth, G. S., "Measuring the color of growing turf with a reflectance spectrophotometer" 60 : 640-643, 1968

25 Zhang, L., "Maize canopy temperature extracted from UAV thermal and RGB imagery and its application in water stress monitoring" 10 : 1-18, 2019

26 Nebiker, S., "Lightweight multispectral UAV sensors and their capabilities for predicting grain yield and detecting plant diseases" 963-970, 2016

27 Cho, S. W., "Influence of different nitrogen application on flour properties, gluten properties by HPLC and end-use quality of Korean wheat" 17 : 982-993, 2018

28 Lelong, C. C. D., "Hyperspectral imaging and stress mapping in agriculture" 66 : 179-191, 1998

29 Luo, L., "How does nitrogen shape plant architecture?" 71 : 4415-4427, 2020

30 Calderón, R., "High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices" 139 : 231-245, 2013

31 Xingyun, L., "Global response patterns of plant photosynthesis to nitrogen addition : A meta-analysis" 26 : 3585-3600, 2019

32 Moran, R, "Formulae for determination of chlorophyllous pigments extracted with N,N-dimethylformamide" 69 : 1376-1381, 1982

33 FAO, "Food outlook – Biannual report on global food markets" 11-16, 2020

34 Zarco-Tejada, P. J., "Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera" 117 : 322-337, 2012

35 Xu, J., "Exploring optimal irrigation and nitrogen fertilization in a winter wheat-summer maize rotation system for improving crop yield and reducing water and nitrogen leaching" 228 : 105904-, 2019

36 Kong, L., "Excessive nitrogen application dampens antioxidant capacity and grain filling in wheat as revealed by metabolic and physiological analyses" 7 : 43363-, 2017

37 Filella, I., "Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis" 35 : 1400-1405, 1995

38 Ashourloo, D., "Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements" 6 : 5107-5123, 2014

39 Li, F., "Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages" 11 : 335-357, 2010

40 Darvishzadeh, R., "Estimation of vegetation LAI from hyperspectral reflectance data : Effects of soil type and plant architecture" 10 : 358-373, 2008

41 Wu, C., "Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation" 148 : 1230-1241, 2008

42 Bendig, J., "Estimating biomass of barley using crop surface models(CSMs)derived from UAV-based RGB Imaging" 6 : 10395-10412, 2014

43 Siegal, B. S., "Effect of vegetation on rock and soil type discrimination, Photogramm" 43 : 191-196, 1977

44 Rumpf, T., "Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance" 74 : 91-99, 2010

45 Cao, X., "Detection of powdery mildew in two winter wheat plant densities and prediction of grain yield using canopy hyperspectral reflectance" 10 : 1-14, 2015

46 Behmann, J., "Detection of early plant stress responses in hyperspectral images" 93 : 98-111, 2014

47 Nutter, F. W, "Detection and measurement of plant disease gradients in peanut with a multispectral radiometer" 79 : 958-963, 1989

48 Mishra, P., "Close range hyperspectral imaging of plants: A review" 164 : 49-67, 2017

49 McKinney, G, "Absorption of light by chlorophyll solutions" 140 : 315-322, 1941

50 Gamon, J. A., "A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency" 41 : 35-44, 1992

51 Reyniers, M., "A linear model to predict with a multi‐spectral radiometer the amount of nitrogen in winter wheat" 27 : 4159-4179, 2006

52 Datt, B, "A New reflectance index for remote sensing of chlorophyll content in higher plants : tests using Eucalyptus leaves" 154 : 30-36, 1999

53 KOSTAT, "2019 Food Grain Consumption Survey" 17-30, 2020