한국농림기상학회지 (Korean Journal of Agricultural and Forest Meteorology)

  • 제23권4호
  • /
  • Pages.316-328
  • /
  • 2021
  • /
  • 1229-5671(pISSN)
  • /
  • 2288-1859(eISSN)
한국농림기상학회 (Korean Society of Agricultural and Forest Meteorology)
권호 표지
DOI QR코드

DOI QR Code

초분광 영상을 이용한 봄감자의 잎 Na 함량 예측 모델 개발

Development of Prediction Model for the Na Content of Leaves of Spring Potatoes Using Hyperspectral Imagery

  • 박준우 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 강예성 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 유찬석 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 장시형 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 강경석 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 김태양 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 박민준 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 백현찬 (경상국립대학교 바이오시스템공학과 (농업생명과학연구원)) ;
  • 송혜영 (농촌진흥청 국립농업과학원) ;
  • 전새롬 (주식회사 호티젠) ;
  • 이수환 (농촌진흥청 국립식량과학원)
  • Park, Jun-Woo (Department of Bio-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Kang, Ye-Seong (Department of Bio-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Ryu, Chan-Seok (Department of Bio-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Jang, Si-Hyeong (Department of Bio-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Kang, Kyung-Suk (Department of Bio-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Kim, Tae-Yang (Department of Bio-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Park, Min-Jun (Department of Bio-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Baek, Hyeon-Chan (Department of Bio-System Engineering, GyeongSang National University (Institute of Agriculture & Life Science)) ;
  • Song, Hye-Young (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Jun, Sae-Rom (Hortizen Co. Ltd.) ;
  • Lee, Su-Hwan (National Institute of Crop Science, Rural Development Administration)
  • 투고 : 2021.11.29
  • 심사 : 2021.12.27
  • 발행 : 2021.12.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구에서는 간척지의 염분 모니터링을 위한 다중 분광 센서를 개발하기 위해 400~1000 nm 초분광센서를 사용하여 봄 감자의 잎 Na 함량 예측 모델을 구축하고자 하였다. 관개조건은 표준, 한해, 염해(2, 4, 8 dS/m)로, 관수량은 증발량을 기준으로 산정하였다. 영양생장기, 괴경형성기, 괴경비대기에 각각 관개를 시작한 후 1주와 2주 후에 잎의 Na 함량을 측정하였다. 잎의 반사율은 10nm 파장 간격을 기준으로 5 nm에서 10nm, 25nm, 50nm FWHM (full width at half maximum)으로 변환되었다. PLS-VIP를 사용하여 봄 감자 잎의 Na 함량에 따른 염분 피해 수준을 예측하기 위한 10개의 밴드비가 선택되었다. 선택된 10개의 밴드비 중 가중치가 가장 낮은 순서대로 밴드비를 하나씩 제거하면서 MLR모델을 추정하였다. 모델의 성능은 R2, MAPE 뿐만 아니라 밴드비의 수, 다중 분광센서를 작게 만들기 위한 최적의 FWHM 수로 비교하였다. 1, 2주차의 영양생장기, 괴경형성기와 2주차의 괴경비대기에서 봄 감자의 잎 Na 함량을 예측하기 위해서는 25 nm의 FWHM을 사용하는 것이 유리하였다. 선택된 밴드필터는 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm로 Red 및 Red-edge 영역에서 15개 밴드비가 선택되었다.

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.

키워드

과제정보

본 연구는 농촌진흥청 연구사업(세부과제명: 영상기법 활용 간척지 작물 염해 예측 기술 개발, 세부과제번호: PJ01388404)의 지원에 의해 이루어진 것임.

참고문헌

  1. Ayers, R. S., and D. W. Westcot, 1985: Water quality for agriculture. Rome: Food and Agriculture Organization of the United Nations, 174pp.
  2. Berni, J. A., P. J. Zarco-Tejada, L. Suarez, and E. Fereres, 2009: Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on geoscience and Remote Sensing 47(3), 722-738. https://doi.org/10.1109/TGRS.2008.2010457
  3. Carter, G. A., 1993: Responses of leaf spectral reflectance to plant stress. American journal of botany 80(3), 239-243. https://doi.org/10.2307/2445346
  4. Chen, S., X. Hong, C. J. Harris, and P. M. Sharkey, 2004: Sparse modeling using orthogonal forward regression with PRESS statistic and regularization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34(2), 898-911. https://doi.org/10.1109/TSMCB.2003.817107
  5. Choi, C. H., K. C. Kim, D. R. Lee, S. H. Cho, D. H. Cho, S. Y. Lee, and I. S. Lee, 2018: Evaluation of various characteristics of high quality rice varieties that could potentially be grown on reclaimed land in Jellabuk Province, Korea. Korean Journal of Crop Science 63(3), 196-204. https://doi.org/10.7740/KJCS.2018.63.3.196
  6. Ekelof, J., 2007: Potato yield and tuber set as affected by phosphorus fertilization.
  7. GRI (Gyeonggi Research Institute), 2007: Analysis and utilization of west coast reclamation. 2007 Report of Gyeonggi Research Institute.
  8. Hamzeh, S., A. A. Naseri, S. K. Alavipanah, B. Mojaradi, H. M. Bartholomeus, J. G. Clevers, and M. Behzad, 2013: Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices. International Journal of Applied Earth Observation and Geoinformation 21, 282-290. https://doi.org/10.1016/j.jag.2012.07.002
  9. Hasegawa, P. M., R. A. Bressan, J. K. Zhu, and H. J. Bohnert, 2000: Plant cellular and molecular responses to high salinity. Annual Review of Plant Physiology and Plant Molecular Biology 51(1), 463-499. https://doi.org/10.1146/annurev.arplant.51.1.463
  10. Huang, M., M. S. Kim, S. R. Delwiche, K. Chao, J. Qin, C. Mo, and Q. Zhu, 2016: Quantitative analysis of melaminein milk powders using near-infrared hyperspectral imaging and band ratio. Journal of Food Engineering 181, 10-19. https://doi.org/10.1016/j.jfoodeng.2016.02.017
  11. Jensen, J. R., 2015: Introductory digital image processing: a remote sensing perspective. Prentice Hall Press.
  12. Kang, K. S., C. S. Ryu, S. H. Jang, Y. S. Kang, S. R. Jun, J. W. Park, H. Y. Song, and S. H. Lee, 2019: Application of hyperspectral imagery to decision tree classifier for assessment of spring potato (Solanum tuberosum) damage by salinity and drought. Korean Journal of Agricultural and Forest Meteorology 21(4), 317-326. https://doi.org/10.5532/KJAFM.2019.21.4.317
  13. 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. Computers and Electronics in Agriculture 178, 105667. https://doi.org/10.1016/j.compag.2020.105667
  14. Kang, Y. S., C. S. Ryu, S. H. Kim, S. R. Jun, S. H. Jang, J. W. Park, and T. K. Sarkar, 2018: Yield prediction of Chinese cabbage (Brassicaceae) using broadband multispectral imagery mounted unmanned aerial system in the air and narrowband hyperspectral imagery on the ground. Journal of Biosystems Engineering 43(2), 138-147. https://doi.org/10.5307/JBE.2018.43.2.138
  15. Koo, J. W., J. K. Choi, and J. G. Son, 1998: Soil properties of reclaimed tidel lands and tidelands of western sea coast in Korea. Korean Journal of Soil Science and Fertilizer 31(2), 120-127.
  16. Lee, S. H., S. H. Yoo, S. I. Seol, Y. An, Y. S Jung, and S. M. Lee., 2000: Assessment of salt damage for upland-crop in Dae-Ho reclaimed soil. Korean Journal of Environment Agriculture 19(4), 358-363. (in Korean with English abstract).
  17. Lee, S., R. NICS, H. Bae, R. NICS, S. H. Lee, R. NICS, and R. NICS, 2016: Effect of irrigation on soil salinity and corn (Zea mays) growth at coarse-textured tidal saline soil. The Journal of the Korean Society of International Agriculture 28(4), 526-532. https://doi.org/10.12719/KSIA.2016.28.4.526
  18. Lewis, C. D., 1982: Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth-Heinemann.
  19. Medjahed, S. A., T. A. Saadi, A. Benyettou, and M. Ouali, 2016: Gray wolf optimizer for hyperspectral band selection. Applied Soft Computing 40, 178-186. https://doi.org/10.1016/j.asoc.2015.09.045
  20. Nigon, T. J., D. J. Mulla, C. J. Rosen, Y. Cohen, V. Alchanatis, J. Knight, and R. Rud, 2015: Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars. Computers and Electronics in Agriculture 112, 36-46. https://doi.org/10.1016/j.compag.2014.12.018
  21. Onoyama, H., C. Ryu, M. Suguri, and M. Iida, 2015: Nitrogen prediction model of rice plant at panicle initiation stage using ground-based hyperspectral imaging: Growing degree-days integrated model. Precision Agriculture 16(5), 558-570. https://doi.org/10.1007/s11119-015-9394-9
  22. Parida, A. K., and A. B. Das, 2005: Salt tolerance and salinity effects on plants: a review. Ecotoxicology and Environmental Safety 60(3), 324-349. https://doi.org/10.1016/j.ecoenv.2004.06.010
  23. Phatak, A., and S. De Jong, 1997: The geometry of partial least squares. Journal of Chemometrics: A Journal of the Chemometrics Society 11(4), 311-338. https://doi.org/10.1002/(SICI)1099-128X(199707)11:4<311::AID-CEM478>3.0.CO;2-4
  24. Richter, M., and J. Beyerer, 2014: Optical filter selection for automatic visual inspection. In IEEE Winter Conference on Applications of Computer Vision, 123-128.
  25. Ruffin, C., and R. L. King, 1999: The analysis of hyperspectral data using Savitzky-Golay filtering-theoretical basis. 1. In IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No. 99CH36293) 2, 756-758.
  26. Savitzky, A., and M. J. Golay, 1964: Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry 36(8), 1627-1639. https://doi.org/10.1021/ac60214a047
  27. Vaiphasa, C., 2006: Consideration of smoothing techniques for hyperspectral remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 60(2), 91-99. https://doi.org/10.1016/j.isprsjprs.2005.11.002
  28. Williams, P., 2003: Near-infrared Technology-Getting the Best Out of Light. PDK Grain., 8-10.
  29. Zygielbaum, A. I., A. A. Gitelson, T. J. Arkebauer, and D. C. Rundquist, 2009: Non-destructive detection of water stress and estimation of relative water content in maize. Geophysical Research Letters 36(12).