Browse > Article
http://dx.doi.org/10.12791/KSBEC.2022.31.4.384

Diagnosis of Nitrogen Content in the Leaves of Apple Tree Using Spectral Imagery  

Jang, Si Hyeong (Fruit Research Division, National institute of Horticultural & Herbal Science)
Cho, Jung Gun (Fruit Research Division, National institute of Horticultural & Herbal Science)
Han, Jeom Hwa (Fruit Research Division, National institute of Horticultural & Herbal Science)
Jeong, Jae Hoon (Fruit Research Division, National institute of Horticultural & Herbal Science)
Lee, Seul Ki (Fruit Research Division, National institute of Horticultural & Herbal Science)
Lee, Dong Yong (Fruit Research Division, National institute of Horticultural & Herbal Science)
Lee, Kwang Sik (Fruit Research Division, National institute of Horticultural & Herbal Science)
Publication Information
Journal of Bio-Environment Control / v.31, no.4, 2022 , pp. 384-392 More about this Journal
Abstract
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.
Keywords
hyperspectral imagery; leaf nutrition; nitrogen fertilizer; linear regression; growth model;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 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   DOI
2 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   DOI
3 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   DOI
4 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)
5 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   DOI
6 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   DOI
7 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   DOI
8 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   DOI
9 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   DOI
10 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   DOI
11 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   DOI
12 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   DOI
13 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   DOI
14 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   DOI
15 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   DOI
16 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.
17 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)
18 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.   DOI
19 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   DOI
20 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   DOI
21 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   DOI
22 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.   DOI