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http://dx.doi.org/10.5532/KJAFM.2021.23.4.316

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

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)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.23, no.4, 2021 , pp. 316-328 More about this Journal
Abstract
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.
Keywords
Hyperspectral imagery; PLS-VIP; MLR; Spring potato; Salinity;
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1 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.   DOI
2 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.
3 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.   DOI
4 Jensen, J. R., 2015: Introductory digital image processing: a remote sensing perspective. Prentice Hall Press.
5 Lewis, C. D., 1982: Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth-Heinemann.
6 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.   DOI
7 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.   DOI
8 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.   DOI
9 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.   DOI
10 Williams, P., 2003: Near-infrared Technology-Getting the Best Out of Light. PDK Grain., 8-10.
11 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).
12 Richter, M., and J. Beyerer, 2014: Optical filter selection for automatic visual inspection. In IEEE Winter Conference on Applications of Computer Vision, 123-128.
13 Savitzky, A., and M. J. Golay, 1964: Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry 36(8), 1627-1639.   DOI
14 Vaiphasa, C., 2006: Consideration of smoothing techniques for hyperspectral remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 60(2), 91-99.   DOI
15 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.   DOI
16 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.   DOI
17 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.   DOI
18 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.   DOI
19 Ayers, R. S., and D. W. Westcot, 1985: Water quality for agriculture. Rome: Food and Agriculture Organization of the United Nations, 174pp.
20 Carter, G. A., 1993: Responses of leaf spectral reflectance to plant stress. American journal of botany 80(3), 239-243.   DOI
21 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.   DOI
22 Ekelof, J., 2007: Potato yield and tuber set as affected by phosphorus fertilization.
23 GRI (Gyeonggi Research Institute), 2007: Analysis and utilization of west coast reclamation. 2007 Report of Gyeonggi Research Institute.
24 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.   DOI
25 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.   DOI
26 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.
27 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).
28 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.   DOI
29 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.   DOI