Assessment of Hyperspectral Reflectance on Rice Leaf Inoculated with Bacterial Leaf Blight for Applicability of UAV Monitoring 361 Introduction 농촌인구감소

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1 Korean J. Soil Sci. Fert. Vol.51, No.4, pp , 2018 Korean Journal of Soil Science and Fertilizer Short communication pissn : eissn : Assessment of Hyperspectral Reflectance on Rice Leaf Inoculated with Bacterial Leaf Blight for Applicability of UAV Monitoring Kyung Do Lee, Sang-Min Kim 1, Chan-Won Park, Kyu-Ho So, Ho-yong An, and Sang Il Na* National Institute of Agricultural Sciences, RDA, Wanju 56365, Korea 1 National Institute of Crop Science, RDA, Wanju 55365, Korea *Corresponding author: sangil917@korea. kr A B S T R A C T Received: June 8, 2018 Revised: November 29, 2018 Accepted: November 29, 2018 We analyzed the spectral reflectance characteristics of rice leaves according to the occurrence of bacterial leaf blight and the correlation between vegetation index and disease area ratio in order to evaluate the possibility of using Unmanned Aerial Vehicle (UAV) image for disease investigation. As a result, relationship between the spectral reflectance of inoculated rice leaves and the ratio of disease area showed a high correlation of 0.9 or more in wavelength from 700 nm to 750 nm. Also, the reflectance of the visible light region from 500 nm to 700 nm and the near infrared region in more than 800 nm showed a correlation from 0.8 to 0.9 more. The area ration of rice leaves increase rapidly after 10 days of inoculation. The vegetation index(ndvi, GNDVI and NDRE) that can be calculated by UAV images showed a tendency to decrease remarkably with increase of diseased area ratio. Relationship between the vegetation index and disease area ratio on the 10 th day after the inoculation, in which disease area ratio increased rapidly, showed a negative linear correlation from to Therefore, it is considered that it is possible to analyze the diseased area by UAV image. Keywords: Hyperspectral reflectance, Bacterial leaf blight, UAV Monitoring Change of spectral reflectance on rice leaves according to days after inoculation of Bacterial Leaf Blight. C The Korean Society of Soil Science and Fertilizer. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non- Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

2 Assessment of Hyperspectral Reflectance on Rice Leaf Inoculated with Bacterial Leaf Blight for Applicability of UAV Monitoring 361 Introduction 농촌인구감소, 고령화에대응하여영농생산기반을안정적으로유지하기위해서는경영효율화측면에서규모화와자동화가필요하다 (Lee et al., 2016). 영농활동의규모화와자동화를위해서는농경지와작물의정보를신속하고과학적으로분석하여제공할필요가있다 (Park et al., 2005; Kim, 2016). 원격탐사는전자기파를감지하는센서를이용하여지표면, 물, 대기현상에대해비접촉, 비파괴적인방법으로정보를얻는기술로서농업분야에서는토양특성분석및농작물작황추정에활용되어왔다 (Hong et al., 1998; Hong et al., 2010; Choi et al., 2010). 최근에는무인비행체기술이급속히발전하면서기존유인항공기및상업용위성에비해저렴하게사용자가원하는시간과공간에서 ha 내외의들녁단위에대해 5 cm 내외의고해상도항공영상을획득할수있게되면서원격탐사기술에기반하여농경지와농작물정보를신속하고과학적으로생산할수있게되었다 (Lee et al., 2016). 무인비행체영상을농업분야에활용한연구로서, Xiang and Tian (2011) 은무인비행체영상을이용하여작물의초장을추정하였으며, Torres-Sanchez et al. (2014) 은무인비행체에카메라를부착하여밀의토지피복량을산정한바있다. Bendig et al. (2014) 은 UAV 기반의 RGB (Red-Green-Blue) 영상을작물모형에적용하여보리를대상으로작황변이를모니터링하고바이오매스를추정한바있다. 국내에서도 Lee et al. (2015) 은무인비행체영상을기반으로풋거름 ( 헤어리베치 ) 재배단지를촬영하고풋거름의질소량을추정한바있으며, Lee et al. (2016) 은 1년간무인비행체영상촬영결과분석을통해무인비행체영상으로잔디및콩에대해산정한 NDVI (Normalized Difference Vegetation Index) 식생지수가지상센서로측정한결과와높은상관관계를보고한바있다. Na et al. (2016a, 2016b) 은무인비행체영상을활용하여고랭지배추, 맥류작황추정연구를수행하였으며, Lee et al. (2017) 은무인비행체영상을활용하여벼생육인자추정시최적식생지수와추정식을제안한바있다. 그러나아직무인비행체영상을활용한작물병해분석연구는미흡한실정이다. 따라서, 본연구는무인비행체영상을활용한작물병해평가의사전연구로서벼흰잎마름병발생에따른벼잎의분광반사특성을분석하고병해조사를위한무인비행체영상의활용가능성을평가하기위하여수행되었다. Materials and Methods 벼흰잎마름병접종, 분광반사및병징면적비율조사벼흰잎마름병은세균에의해발병되는병으로우리나라를비롯하여일본, 대만, 필리핀, 인도, 인도네시아등동남아의벼재배지에서큰문제가되고있는병해이다 (Noh et al., 2007). 벼흰잎마름병접종및병해진전에따른분광반사특성을분석하기위해 2016년 12월 14일국립식량과학원온실에서벼흰잎마름병감수성품종인 동진1호 를사각포트에이앙하여 2017년 3월 3일벼흰잎마름병 (K3a 레이스 ) 균배양액에가위를적신후절엽하여병을접종하였다. 2017년 3월 3일병접종직전과접종후 3, 5, 7, 10, 12, 17, 24일이경과한 3월 6일, 3월 8일, 3월 10일, 3월 13일, 3월 15일, 3월 20일, 3월 27일총 8회에걸쳐포트에서벼 6주에대해벼잎을 1본씩 6본을채취하였다. 대조구는병균에접촉되지않은가위로 3월 3일절엽한후병접종시료채취일자와동일한날짜에벼 2주에대해벼잎을 1본씩 2본을채취하였다. 분광반사스펙트럼은분광복사계 ASD FieldSpec Pro (Analytical Spectral Devices Inc., USA) 에 Plant Probe를장착하여절엽부위에서 5 cm 밑부분을 nm 범위에서 1 nm 간격으로반사율을측정하였다. Plant probe는

362 Korean Journal of Soil Science and Fertilizer Vol. 51, No. 4, 2018 벼잎분광반사율측정시외부영향을차단하기위해밀폐되는구조로제작되었으며, 할로겐램프에서빛을균일하게방사하여식물체잎에서반사되는에너지를감지한다.

, USA) 로스캔하여 Probe 측정부분에대해 ArcGIS (ESRI, USA) 프로그램을이용하여병징이판별되는부분의경계를작도 (Digitizing) 한후산정하였다. 병발생면적비율과분광반사율, 식생지수상관성분석농경지와작물의상황을정성적으로분석하거나한시기의영상만을이용할경우촬영된항공영상그대로를이용할수있다.

3 362 Korean Journal of Soil Science and Fertilizer Vol. 51, No. 4, 2018 벼잎분광반사율측정시외부영향을차단하기위해밀폐되는구조로제작되었으며, 할로겐램프에서빛을균일하게방사하여식물체잎에서반사되는에너지를감지한다. 할로겐램프와벼잎측정부위뒷면은백색판 (White Reference) 이설치되어있어벼잎측정전후로반사율값을일정하게보정하여실험을수행하였다 (Fig. 1). 측정상스펙트럼의잡음과오차를최소화하기위해매측정시마다스펙트럼을 3회반복측정하여평균하였다 (Choi et al., 2009). 벼잎의병징면적비율은채취한벼잎을스캐너 (HP inc., USA) 로스캔하여 Probe 측정부분에대해 ArcGIS (ESRI, USA) 프로그램을이용하여병징이판별되는부분의경계를작도 (Digitizing) 한후산정하였다. 병발생면적비율과분광반사율, 식생지수상관성분석농경지와작물의상황을정성적으로분석하거나한시기의영상만을이용할경우촬영된항공영상그대로를이용할수있다. 그러나시계열적작물변화상황을정량적으로분석하기위해서는촬영시점의기상, 태양고도, 카메라특성등다양한촬영환경에따라촬영된영상데이터값 (DN :Digital Number) 이변화될수있으므로식물체고유반사율값 (Reflectance) 과단위가없는복사값으로식생활력도와생육현황을나타내는지표인식생지수 (Vegetation Index) 산정과활용이필요하다 (Cohen, 1991; Lee et al., 2016). Lee et al. (2016) 은 2015년부터 2016년까지 1년동안 20회무인비행체항공영상촬영을통해다양한지표면물체를대상으로항공영상반사율과대표적인식생지수중하나인 NDVI (Normalized Difference Vegetation Index) 를 Fig. 1. Photo of spectral reflectance measurement. Fig. 2. Symptoms change of Bacterial Leaf Blight in rice leaf with time after inoculation.

4 Assessment of Hyperspectral Reflectance on Rice Leaf Inoculated with Bacterial Leaf Blight for Applicability of UAV Monitoring 363 산정하여지상에서분광복사계로측정한값과비교하였다. 그결과항공영상으로산정한반사율경우지상에서측정한분광복사계반사율과일정한경향을보이지않았으며, 기상, 광환경등촬영환경차이에따른반사율편차도컸으나가시광선영역과근적외선영역의반사율비율식으로계산되는 NDVI 경우촬영환경조건에큰영향없이지상센서에서측정한값과유사한변화경향과높은상관관계를보인다고보고하였다. 따라서본연구에서는무인비행체영상을활용한병발생추정가능성을평가하기위해병접종벼잎에대한분광반사율측정값중에서최근농작물작황평가에많이활용되고있는무인비행체탑재카메라 (Table 1) 의중심파장에해당하는값을활용하여산정가능하고, 기존위성및유인비행체를활용한병해탐지연구 (Das et al., 2016; Qin and Zhan, 2005) 에사용된식생지수산정방식을참고하여 Table 2와같이 NDVI, GNDVI, NDRE 식생지수를선정하여병징면적비율과상관성을분석하였다. Table 1. Specification of mainly camera mounted on UAV for crop condition monitoring. Camera Model (Manufacturer) S110 (Cannon) RedEdge (Micasense) Band type Green Red NIR Green Red RedEdge NIR Center wavelength (nm) Utilization case study of UAV application in agriculture Nitrogen in plant (Lee et al., 2015), Rice, Kimchi cabbage, Barely monitoring (Na et al., 2016a; 2016b; Lee et al., 2017a; 2017b) Growth monitoring in vegetation, wheat, beans (Aasen et al, 2015; Zhou et al., 2016; Duan et al., 2017) Table 2. Vegetation index that can be calculated and be used mainly by imagery of camera mounted on UAV. Name Equation* Camera Model NDVI (Normalized Difference Vegetation Index) (Rn-Rr)/(Rn+Rr) S110, RedEdge GNDVI (Greent Normalized Difference Vegetation Index (Rn-Rg)/(Rn+Rg) S110, RedEdge NDRE (Normalized Difference Vegetation Index) (Rn-Redg)/(Rn+Redg) RedEdge *R λ the reflectance at wavelength λ; R n, R r, R g, and R edg are the reflectances for NIR, red, green, and rededge bands, respectively. Results and Discussions 병징확산에따른벼잎분광반사특성변화벼흰잎마름병접종후경과일수에따른벼잎분광반사율변화는 Fig. 3과같았다. 2017년 3월 3일병접종전채취한벼잎분광반사율은가시광선영역 (425 nm-740 nm) 중청색영역 (425 nm-490 nm) 과적색영역 (640 nm-740 nm) 에서는낮은값을보이고녹색영역 (490 nm-585 nm) 에서는청색, 적색에비해높은반사율값을보였다. 근적외선영역 (740 nm-1300 nm) 은가시광선에비해높은반사율을보였으며이후단파적외선영역 (1300 nm-1800 nm) 에서는 1400 nm 근방에서감소하다다시증가하는경향을보였다. 중적외선영역 (1800 nm-2500 nm) 의경우 1900 nm파장에서반사율값이감소한이후 2200 nm 파장까지증가하다이후 2400 nm 파장에서감소하는경향을보였다. 건강한벼잎의이러한분광반사특성은엽록소가녹색영역파장에비해청색과적색영역파장을잘흡수하며, 식물옆세포구조및특성이근적외선영역을잘반사하고, 옆수분함량

5 364 Korean Journal of Soil Science and Fertilizer Vol. 51, No. 4, 2018 이높을수록단파적외선, 중적외선일부파장구간 (1400 nm, 1900 nm, 2500 nm) 의빛흡수가증가하기때문이다 (Jense, 2005; Hong et al., 1998 ). 병접종후 7일이경과한 2017년 3월 10일까지는측정지점에서가시광선영역의경우분광반사율차이를보이지않았다. 이는분광반사율모니터링을이용한병발생분석은식물체엽록소및수분감소등으로인한엽색변화등외부병징에의한간접적추정방법으로식물체에잠복한병을조기에분석하기는어렵기때문으로판단된다 (Mutka and Bart, 2014). 병접종후 10일이경과한 2017년 3월 13일벼잎분광반사율은 470 nm에서 690 nm까지가시광선영역에서증가하는경향을보였는데특히, 황색영역 (560 nm-640 nm) 파장대에서뚜렷한증가경향을보였다. 또한, 근적외선영역은 740 nm에서 900 nm까지감소하는경향을보였으며, 1400 nm 및 1900 nm 부근에서증가경향을보였다. 병접종이후 12일이경과한 2017년 3월 15일은 3월 13일에비해가시광선영역과근적외선영역모두에서반사율이증가하는경향을보였으며, 병접종후 17일, 24일이경과한 2017년 3월 20일, 27일의경우측정파장영역전체에서반사율이크게증가하는경향을보였다. 병접종후 10일이경과한경우가시광선영역반사율은증가하고, 근적외선영역반사율은감소하였는데이는벼잎집무늬마름병에따른분광반사특성연구 (Qin and Zhang, 2005), 세균성병해에따른사탕수수분광반사특성연구 (Mahlein et al., 2010), 벼흰잎마름병에발생에따른벼군락의분광반사특성연구 (Yang, 2010), 혹명나방피해에따른벼잎분광반사특성연구 (Huang et al., 2012) 와동일한결과였다. 병접종후 12일이후부터병징면적비율이증가하면서가시광선영역반사율은지속적으로증가하였으며근적외선영역반사율의경우병접종후 10일에는감소하였으나병접종후 12일의경우다시증가하는경향을보였다. 이러한결과는 Plant probe로세균성병해에따른사탕수수의분광반사특성을조사한 Mahlein et al. (2010) 의연구결과와일치하였으나작물군락의병해진전에따라근적외선영역반사율이지속적으로감소했다는 Qin and Zhang (2005), Yang (2010), Huang et al. (2012) 의연구결과와는다른결과를보였다. 이는작물군락의경우병해진전에따라엽록소가감소하면서입사되는빛에너지의투과가증가하여반사율이지속적으로감소한반면, Plant probe를이용하여벼단위잎의분광반사율을측정할경우할로겐램프에서입사되는빛에너지가병해진전에따라벼잎의흡수능이저하되면서벼잎을투과한후벼잎뒷면에설치된 Plant probe 끝부분백색판에서다시반사되었기때문으로판단된다. Fig. 3. Change of spectral reflectance on rice leaves according to days after inoculation of Bacterial Leaf Blight.

6 Assessment of Hyperspectral Reflectance on Rice Leaf Inoculated with Bacterial Leaf Blight for Applicability of UAV Monitoring 365 벼잎병징면적비율과분광반사율및식생지수의상관관계분광반사율측정지점에서병징면적비율과분광반사율과의상관성을분석한결과는 Fig. 4와같았다. 전기간동안병징면적비율과분광반사율간의상관계수는 705 nm에선 0.92로가장높은값을보였으며, 1370 nm, 1850 nm, 505 nm 순이었다. 이러한결과는세균성병해에의한사탕수수잎의분광반사율을 350 nm에서 1100 nm의범위에서측정한 Mahlein et al. (2010) 의연구에서병해발생정도와반사율의상관계수가 710 nm에서가장높았다는보고와유사한결과이다. 병접종이후병징면적비율이급격히증가하는 3월 13일까지의분광반사율과병징면적비율간의상관계수는 695 nm파장대역에서 0.99로가장높았으며 620 nm, 1390 nm, 1860 nm에서도 0.95 이상의높은값을보였다. 또한 745 nm에서도 -0.97의높은음의상관을보였다. 이러한결과는벼흰잎마름병에감수성인품종에대해병발생에따른군락의분광반사율을측정한 Yang (2010) 의연구에서병징면적비율과분광반사율의상관은 745 nm 파장에서 -0.96으로가장높은음의상관을보였으며 550 nm 부근과 700 nm 부근및 NIR 영역에서상관성이높았다는보고와유사한결과이다. 분광반사율측정대상이단위잎인지또는군락인지에따라서또한측정방법과병종류에따라서병해발생면적비율과분광반사율의상관성은차이를보였으나대체적으로 700 nm에서 750 nm에서 0.9 이상의높은상관관계를보였으며 500 nm에서 700 nm 사이의가시광선영역과 800 nm 이후근적외선영역에서도 이상의비교적높은상관성을보이는것으로나타났다. 무인비행체에탑재되어많이활용되고있는카메라모델 (S110, RedEdge) 로획득가능한항공영상의중심파장으로산정한주요식생지수 (NDVI, GNDVI, NDRE) 와병해조사면적비율변화는 Fig. 5와같았다. 병접종처리한잎의병발생면적비율은병접종 10일이후부터급격히증가하였으며, 식생지수의경우병발생면적증가와함께뚜렷이감소하는경향을보였다. NDVI의경우카메라모델에관계없이초기에 GNDVI에비해높은값을보이다병발생면적비율이급격히증가하는시점을기준으로수치가급격히감소하여후기에는 GNDVI에비해낮은값을보였다. 카메라종류에따른식생지수의차이는크지않았는데이는 S110 카메라와 RedEdge 카메라의중심파장이 Green, Red, NIR 영역에서차이가크지않았기때문으로판단된다. NDRE의경우 의범위로 NDVI 및 GNDVI에비해낮은수치를보였으나병발생면적비율이급격히증가했던병접종후 10일을경계로전후식생지수값변동률은 NDVI 및 GNDVI가 10-20% 였던반면 NDRE의경우 46% 로높은값을보였다. (a) Whole period (b) Up to 10 days after inoculation Fig. 4. Diagram of coefficient of correlation for the linear correlation between spectral reflectance of Bacterial Leaf Blight and proportion of infested area (%) in relation to wavelength.

7 366 Korean Journal of Soil Science and Fertilizer Vol. 51, No. 4, 2018 (a) Inoculation treatment (b) Control Fig. 5. Change of proportion of infested area(%) on rice leaves and vegetation index by camera type. (a) S110 camera (left: NDVI, right : GNDVI) (b) RedEdge camera (left: NDVI, right : GNDVI) (c) RedEdge camera (NDRE) Fig. 6. Correlation between vegetation index and proportion of infested area(%).

8 Assessment of Hyperspectral Reflectance on Rice Leaf Inoculated with Bacterial Leaf Blight for Applicability of UAV Monitoring 367 병접종을하지않은대조구의경우실험기간동안병해발생면적비율은대체적으로 5% 이하였으나병발생후 24 일이경과한이후에는병해발생면적이증가하는경향을보였다. 이는병처리구의벼병해가진전되면서시험후기에병처리구와인접한대조구에서도일부병감염이발생했기때문으로판단된다. 병징면적비율이빠르게증가했던병접종후 10일 (2017년 3월 13일 ) 의식생지수와병징면적비율의관계는 Fig. 6과같았다. NDVI, GNDVI 및 NDRE와병징면적비율의상관계수는 -0.77에서 -0.95으로음의선형상관을보여무인비행체로촬영한항공영상의식생지수로병징면적분석가능성을확인할수있었다. 이는이들식생지수가앞서분석한분광반사율분석에서병징비율과상관성이높은 500 nm에서 700 nm 사이의가시광선영역과 800 nm 이후근적외선영역의비율로계산되었기때문으로판단된다. Conclusions 벼흰잎마름병발생에따른벼잎의분광반사특성을분석하고병해조사를위한무인비행체영상의활용가능성을평가하기위하여영상기반식생지수와병징면적비율의상관관계를분석하였다. 병접종벼잎의분광반사율변화를분석한결과병징면적비율은 700 nm에서 750 nm의파장대역반사율과 0.9 이상의높은상관관계를보였으며 500 nm에서 700 nm 사이가시광선영역과 800 nm 이후근적외선영역반사율과도 이상의비교적높은상관성을보이는것으로나타났다. 벼흰잎마름병접종처리한벼잎병징면적비율은병접종 10일이후부터급격히증가하였으며, NDVI, GNDVI 및 NDRE 식생지수는병징면적비율증가와함께뚜렷이감소하는경향을보였다. 병징면적비율이빠르게증가했던병접종후 10일의식생지수와병징면적비율의상관계수는 -0.77에서 -0.95로음의선형상관을보여무인비행체로촬영한항공영상식생지수로병징면적분석가능성을확인할수있었다. Acknowledgement This work was carried out with the support of Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ ) Rural Development Administration, Republic of Korea. References Aasen, H., A. Burkart, A. Bolten, and G. Bareth Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS J. Photogramm. Remote Sens. 108: Bendig, J., A. Bolten, S. Bennertz, J. Broscheit, S. Eichfuss, and G. Bareth Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging, Remote Sens. 6(11): Choi, E.Y., S.Y. Hong, Y.H. Kim, G.C. Song, and Y.S. Jang Quantification of soil properties using visible-nearinfrared reflectance spectroscopy. Korean J. Soil Sci. Fert. 42(6): (in Korean). Cohen, W.B Response of vegetation indices to change in three measures of leaf water stress. Photogramm. Eng. Remote Sensing. 57(2): Das, P.K., B. Laxman, S.K. Rao, M.S. Seshasa, and V.K. Dadhwal Monitoring of bacterial leaf blight in rice using ground-based hyperspectral and LISS IV satellite data in Kurnool, Andhra Pradesh, India. Int. J. Pest Manag. 61(4):

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09È«¼®¿µ5~152s Korean Journal of Remote Sensing, Vol.23, No.2, 2007, pp.45~52 Measurement of Backscattering Coefficients of Rice Canopy Using a Ground Polarimetric Scatterometer System Suk-Young Hong*, Jin-Young Hong**,