본 연구는 RGB, 초분광 센서를 이용하여 시기별 사과 잎의 엽록소와 질소 함량을 예측하여 사과 나무 잎의 질소 영양을 진단하기 위해 수행되었다. 분광 데이터는 사과나무 '홍로/M.9' 2년생을 대상으로 고해상도 RGB와 초분광 센서로 촬영 후 영상처리를 통해 취득하였다. 식물체 데이터는 촬영이 끝난직후 엽록소와 잎 질소 함량을 측정하였다. 엽록소 측정기의 SPAD meter, RGB 센서의 개별 파장, 컬러 식생지수 및 초분광 센서의 214개의 파장과 식물체 데이터를 이용하여 회귀분석을 실시하였다. 엽록소와 잎 질소 함량 데이터는 시기와 상관없이 질소 시비량에 따라 통계적으로 유의한 차이가 나타났다. 잎은 시기가 지나면서 잎에 있던 영양분이 과실로 전이되어 색이 옅어졌으며 RGB센서의 경우 Red파장에서 시기와 상관없이 통계적으로 유의한 차이가 나타났다. 초분광 센서의 경우 두 시기 모두 질소 시비 수준에 따라 가시광 영역보다 비가시광 영역에서 차이가 크게 나타났다. 반사값를 이용하여 식물체 특성의 예측 모델 결과 엽록소, 잎 질소함량 모두 초분광 데이터를 이용한 부분최소제곱회귀분석을 이용하였을 때 성능이 가장 높게 나타났다(chlorophyll: 81% / 63%, leaf nitrogen content: 81% / 67%). 이러한 원인은 RGB 센서에 비해 초분광 센서는 좁은 FWHM과 400-1,000nm의 넓은 파장 범위를 가지고 있어 질소 결핍에 의한 스트레스로 인해 작물의 분광학적 해석이 가능했을 것으로 판단된다. 추후 분광학적 특성을 이용하여 전 생육 시기의 수체 생리, 생태 모델 개발 및 검증 그리고 병해충 진단 등 연구를 통해 고품질, 안정적인 과실 생산 기술 개발에 기여될 것으로 사료된다.
본 연구는 RGB, 초분광 센서를 이용하여 시기별 사과 잎의 엽록소와 질소 함량을 예측하여 사과 나무 잎의 질소 영양을 진단하기 위해 수행되었다. 분광 데이터는 사과나무 '홍로/M.9' 2년생을 대상으로 고해상도 RGB와 초분광 센서로 촬영 후 영상처리를 통해 취득하였다. 식물체 데이터는 촬영이 끝난직후 엽록소와 잎 질소 함량을 측정하였다. 엽록소 측정기의 SPAD meter, RGB 센서의 개별 파장, 컬러 식생지수 및 초분광 센서의 214개의 파장과 식물체 데이터를 이용하여 회귀분석을 실시하였다. 엽록소와 잎 질소 함량 데이터는 시기와 상관없이 질소 시비량에 따라 통계적으로 유의한 차이가 나타났다. 잎은 시기가 지나면서 잎에 있던 영양분이 과실로 전이되어 색이 옅어졌으며 RGB센서의 경우 Red파장에서 시기와 상관없이 통계적으로 유의한 차이가 나타났다. 초분광 센서의 경우 두 시기 모두 질소 시비 수준에 따라 가시광 영역보다 비가시광 영역에서 차이가 크게 나타났다. 반사값를 이용하여 식물체 특성의 예측 모델 결과 엽록소, 잎 질소함량 모두 초분광 데이터를 이용한 부분최소제곱회귀분석을 이용하였을 때 성능이 가장 높게 나타났다(chlorophyll: 81% / 63%, leaf nitrogen content: 81% / 67%). 이러한 원인은 RGB 센서에 비해 초분광 센서는 좁은 FWHM과 400-1,000nm의 넓은 파장 범위를 가지고 있어 질소 결핍에 의한 스트레스로 인해 작물의 분광학적 해석이 가능했을 것으로 판단된다. 추후 분광학적 특성을 이용하여 전 생육 시기의 수체 생리, 생태 모델 개발 및 검증 그리고 병해충 진단 등 연구를 통해 고품질, 안정적인 과실 생산 기술 개발에 기여될 것으로 사료된다.
The objective of this study was to estimated nitrogen content and chlorophyll using RGB, Hyperspectral sensors to diagnose of nitrogen nutrition in apple tree leaves. Spectral data were acquired through image processing after shooting with high resolution RGB and hyperspectral sensor for two-year-ol...
The objective of this study was to estimated nitrogen content and chlorophyll using RGB, Hyperspectral sensors to diagnose of nitrogen nutrition in apple tree leaves. Spectral data were acquired through image processing after shooting with high resolution RGB and hyperspectral sensor for two-year-old 'Hongro/M.9' apple. Growth data measured chlorophyll and leaf nitrogen content (LNC) immediately after shooting. The growth model was developed by using regression analysis (simple, multi, partial least squared) with growth data (chlorophyll, LNC) and spectral data (SPAD meter, color vegetation index, wavelength). As a result, chlorophyll and LNC showed a statistically significant difference according to nitrogen fertilizer level regardless of date. Leaf color became pale as the nutrients in the leaf were transferred to the fruit as over time. RGB sensor showed a statistically significant difference at the red wavelength regardless of the date. Also hyperspectral sensor showed a spectral difference depend on nitrogen fertilizer level for non-visible wavelength than visible wavelength at June 10th and July 14th. The estimation model performance of chlorophyll, LNC showed Partial least squared regression using hyperspectral data better than Simple and multiple linear regression using RGB data (Chlorophyll R2: 81%, LNC: 81%). The reason is that hyperspectral sensor has a narrow Full Half at Width Maximum (FWHM) and broad wavelength range (400-1,000 nm), so it is thought that the spectral analysis of crop was possible due to stress cause by nitrogen deficiency. In future study, it is thought that it will contribute to development of high quality and stable fruit production technology by diagnosis model of physiology and pest for all growth stage of tree using hyperspectral imagery.
The objective of this study was to estimated nitrogen content and chlorophyll using RGB, Hyperspectral sensors to diagnose of nitrogen nutrition in apple tree leaves. Spectral data were acquired through image processing after shooting with high resolution RGB and hyperspectral sensor for two-year-old 'Hongro/M.9' apple. Growth data measured chlorophyll and leaf nitrogen content (LNC) immediately after shooting. The growth model was developed by using regression analysis (simple, multi, partial least squared) with growth data (chlorophyll, LNC) and spectral data (SPAD meter, color vegetation index, wavelength). As a result, chlorophyll and LNC showed a statistically significant difference according to nitrogen fertilizer level regardless of date. Leaf color became pale as the nutrients in the leaf were transferred to the fruit as over time. RGB sensor showed a statistically significant difference at the red wavelength regardless of the date. Also hyperspectral sensor showed a spectral difference depend on nitrogen fertilizer level for non-visible wavelength than visible wavelength at June 10th and July 14th. The estimation model performance of chlorophyll, LNC showed Partial least squared regression using hyperspectral data better than Simple and multiple linear regression using RGB data (Chlorophyll R2: 81%, LNC: 81%). The reason is that hyperspectral sensor has a narrow Full Half at Width Maximum (FWHM) and broad wavelength range (400-1,000 nm), so it is thought that the spectral analysis of crop was possible due to stress cause by nitrogen deficiency. In future study, it is thought that it will contribute to development of high quality and stable fruit production technology by diagnosis model of physiology and pest for all growth stage of tree using hyperspectral imagery.
Addink E.A., S.M. de Jong, and E.J. Pebesma 2007, The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery. Photogramm Eng Remote Sens 73:905-912. doi:10.14358/PERS.73.8.905
Brisco B., R.J. Brown, T. Hirose, H. McNairn, and K. Staenz 1998, Precision agriculture and the role of remote sensing: a review. Can J Remote Sens 24:315-327. doi:10.1080/07038992.1998.10855254.
Cheng L., and L.H. Fuchigami 2002, Growth of young apple trees in relation to reserve nitrogen and carbohydrates. Tree Physiol 22:1297-1303. doi:10.1093/treephys/22.18.1297
Choi J.S., and J.M. Choi 1998, Effect on nitrogen fertilization levels and irrigation on calcium content in apple fruits. J Nat Sci 11:113-117. (in Korean)
Jang S.H., C.S. Ryu, Y.S. Kang, S.R. Jun, J.W. Park, H.Y. Song, K.S. Kang, D.O. Kang, K. Zou, and T.H. Jun 2019, Estimation of fresh weight, dry weight, and leaf area index of soybean plant using multispectral camera mounted on rotorwing UAV. Korean J Agric For Meteorol 21:327-336. (in Korean) doi:10.5532/KJAFM.2019.21.4.327
Judkins W.P., and I.W Wanders 1950, Correlation between leaf color, leaf nitrogen content, and growth of apple, peach, and grape plants. Plant Physiol 25:78. doi:10.1104/pp.25.1.78
Kang Y.S., S.H. Jang, J.W. Park, H.Y. Song, C.S. Ryu, S.R. Jun, and S.H. Kim 2020, Yield prediction and validation of onion (Allium cepa L.) using key variables in narrowband hyperspectral imagery and effective accumulated temperature. Comput Electron Agric 178:105667. doi:10.1016/j.compag.2020.105667
Kim S.H., J.G. Kang, C.S. Ryu, Y.S. Kang, T.K. Sarkar, D.H. Kang, Y.G. Ku, and D.E. Kim 2018, Estimation of moisture content in cucumber and watermelon seedlings using hyperspectral imagery. Protected Hort Plant Fac 27:34-39. (in Korean) doi:10.12791/KSBEC.2018.27.1.34
Lee K.D., S.I. Na, S.C. Baek, K.D. Park, J.S. Choi, S.J. Kim, H.J. Kim, H.S. Yun, and S.Y. Hong 2015, Estimating the amount of nitrogen in hairy vetch on paddy fields using unmanned aerial vehicle imagery. Korean J Soil Sci Fertil 48:384-390. (in Korean) doi:10.7745/KJSSF.2015.48.5.384
Park J., J. Park, and I. Lee 2007, Seasonal diagnosis of nitrogen status of 'Fuji'/M.26 apple leaves using chlorophyll meter. Hortic Sci Technol 25:59-62. (in Korean)
Raese J.T., and M.W. Williams 1974, The relationship between fruit color of 'Golden Delicious' apples and nitrogen content and color of leaves. J Amer Soc Hort Sci 99:332-334.
Roussos P.A., and D. Gasparatos 2009, Apple tree growth and overall fruit quality under organic and conventional orchard management. Sci Hortic 123:247-252. doi:10.1016/j.scienta.2009.09.011
Sishodia R.P., R.L. Ray and S.K. Singh 2020, Applications of remote sensing in precision agriculture: A review. Remote Sens 12:3136. doi:10.3390/rs12193136
Song A., W. Jeon, and Y. Kim 2017, Study of prediction model improvement for apple soluble solids content using a ground-based hyperspectral scanner. Korean J Remote Sens 33:559-570. (in Korean) doi:10.7780/kjrs.2017.33.5.1.9
Song S., D. Gibson, S. Ahmadzadeh, H.O. Chu, B. Warden, R. Overend, F. Macfarlane, P. Murray, S. Marshall, M. Aitkenhead, D. Bienkowski, and R. Allison 2020, Low-cost hyper-spectral imaging system using a linear variable bandpass filter for agritech applications. Appl Opt 59:167-175. doi:10.1364/AO.378269
Tian Y.C., X. Yao, J. Yang, W.X. Cao, D.B. Hannaway, and Y. Zhu 2011, Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground-and space-based hyperspectral reflectance. Field Crop Res 120:299-310. doi:1016/j.fcr.2010.11.002
Treder W., K. Klamkowski, W. Kowalczyk, D. Sas, and K. Wojcik 2016, Possibilities of using image analysis to estimate the nitrogen nutrition status of apple trees. ZemdirbysteAgric 103:319-326. doi:10.13080/z-a.2016.103.041
Vigier B.J., E. Pattey, and I.B. Strachan 2004, Narrowband vegetation indexes and detection of disease damage in soybeans. IEEE Geosci Remote Sens Lett 1:255-259. doi:10.1109/LGRS.2004.833776
Walczykowski P., K. Siok, and A. Jenerowicz 2016, Methodology for determining optimal exposure parameters of a hyperspectral scanning sensor. Int Arch Photogramm Remote Sens Spat Inf Sci 41:1065-1069. doi:10.5194/isprsarchives-XLI-B1-1065-2016
Walsh O.S., S. Shafian, J.M. Marshall, C. Jackson, J.R. McClintick-Chess, S.M. Blanscet, K. Swoboda, C. Thompson, K.M. Belmont, and W.L. Walsh 2018, Assessment of UAV based vegetation indices for nitrogen concentration estimation in spring wheat. Adv Remote Sens 7:71-90. doi:10.4236/ars.2018.72006
Ye X., S. Abe, and S. Zhang 2020, Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging. Precis Agric 21:198-225. doi:10.1007/s11119-019-09661-x
Zhao J., S. Vittayapadung, Q. Chen, S. Chaitep, and R. Chuaviroj 2009, Nondestructive measurement of sugar content of apple using hyperspectral imaging technique. Maejo Int J Sci Technol 3:130-142.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.