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2018 Vol.36, Issue 6 Preview Page

Research Article

Shortwave Infrared Hyperspectral Imaging Can Predict Moisture Content in Grafted Cucumber Seedlings
Sung Hyuk Jang1
,
Yong Kee Hwang1
,
Ho Jun Lee2
,
Jae Su Lee23
,
Won Suk Lee4
,
Yong Hyeon Kim1,5*
1Institute for Agricultural Machinery & ICT Convergence, Chonbuk National University
2Department of Agricultural Machinery Engineering, Graduate School, Chonbuk National University
3Farming Automation Division, Department of Agricultural Engineering, National Institute of Agricultural Sciences, RDA
4Department of Agricultural & Biological Engineering, University of Florida
Department of Bioindustrial Machinery Engineering, College of Agriculture & Life Sciences, Chonbuk National University
*교신저자. *Corresponding Author. 
31 December 2018. pp. 831-840
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Abstract

This study aimed to develop statistical model for predicting the moisture content in grafted cucumber (Cucumis sativus L.) by analyzing shortwave infrared hyperspectral images of scions of cucumber seedlings in the range of 1,000-2,400 nm. These images were processed using brightness correction and two sets of data were created by normalizing the spectral reflectance and conducting the first derivative. Statistical analysis of the spectral reflectance and moisture content of the grafted cucumber scions was used to interpret the correlation coefficient spectrum, multiple linear regression, and partial least square (PLS) regression to select the significant wavelengths and develop a model that could predict moisture content. The normalized PLS regression model showed better prediction performance than the other analysis methods tested. The determination coefficient and the root mean square difference for the validation dataset were 0.66 and 1.25%, respectively. These results suggest that the moisture content of grafted cucumber seedlings can be predicted in a nondestructive manner using hyperspectral images with a proper model.

Keywords
partial least square (PLS)
preprocessing
reflectance
statistical model
wavelength

References
1
Apan A, Held A, Phinn S, Markley J (2004) Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery. Int J Remote Sens 25:489-498. doi:10.1080/01431160310001618031
2
Behmann J, Steinrücken J, Plümer L (2014) Detection of early plant stress responses in hyperspectral images. ISPRS J Photogramm Remote Sens 93:98-111. doi:10.1016/j.isprsjprs.2014.03.016
3
Bresson J, Vasseur F, Dauzat M, Koch G, Granier C, Vile D (2015) Quantifying spatial heterogeneity of chlorophyll fluorescence during plant growth and in response to water stress. Plant Methods 11:23. doi:10.1186/s13007-015-0067-5
4
Carter GA (1991) Primary and secondary effects of water content on the spectral reflectance of leaves. Am J Bot 78:916-924. doi:10.2307/2445170
5
Curran PJ (1989) Remote sensing of foliar chemistry. Remote Sens Environ 30:271–278. doi:10.1016/0034-4257(89)90069-2
6
Eitel JU, Gessler PE, Smith AM, Robberecht R (2006) Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. For Ecol Manag 229:170-182. doi:10.1016/j.foreco.2006.03.027
7
Gao BC, Goetz AFH (1990) Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data. J Geophys Res Atoms 95:3549–3564. doi:10.1029/JD095iD04p03549
8
Han M, Zhang H, DeJonge KC, Comas LH, Trout TJ (2016) Estimating maize water stress by standard deviation of canopy temperature in thermal imagery. Agric Water Manag 177:400-409. doi:10.1016/j.agwat.2016.08.031
9
Hazrati S, Tahmasebi-Sarvestani Z, Modarres-Sanavy SAM, Mokhtassi-Bidgoli A, Nicola S (2016) Effects of water stress and light intensity on chlorophyll fluorescence parameters and pigments of Aloe vera L. Plant Physiol Biochem 106:141-148. doi:10.1016/j.plaphy.2016.04.046
10
Jones CD, Jones JB, Lee WS (2010) Diagnosis of bacterial spot of tomato using spectral signatures. Comput Electron Agric 74:329-335. doi:10.1016/j.compag.2010.09.008
11
Kim YH (1996) Effects of air temperature, relative humidity and photosynthetic photon flux on the evapotranspiration rate of grafted seedlings under artificial lighting. In C Kubota, C Chun, eds, Transplant production in the 21st century. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp 91-97
12
Lichtenthaler HK, Babani F (2000) Detection of photosynthetic activity and water stress by imaging the red chlorophyll fluorescence. Plant Physiol Biochem 38:889-895. doi:10.1016/S0981-9428(00)01199-2
13
Lobell DB, Asner GP (2002) Moisture effects on soil reflectance. Soil Sci Soc Am J 66:722-727. doi:10.2136/sssaj2002.7220
14
Mangus DL, Sharda A, Zhang N (2016) Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse. Comput Electron Agric 121:149-159. doi:10.1016/j.compag. 2015.12.007
15
Martynenko A, Shotton K, Astatkie T, Petrash G, Fowler C, Neily W, Critchley AT (2016) Thermal imaging of soybean response to drought stress: the effect of Ascophyllum nodosum seaweed extract. SpringerPlus 5:1393. doi:10.1186/s40064-016-3019-2
16
Min M, Lee WS (2005) Determination of significant wavelengths and prediction of nitrogen content for citrus. Trans ASAE 48:455-461. doi:10.13031/2013.18308
17
Mishra P, Asaari MSM, Langreo AH, Lohumi S, Diezma B, Scheunders P (2017) Close range hyperspectral imaging of plants: A review. Biosyst Eng 164:49-67. doi:10.1016/j.biosystemseng.2017.09.009
18
Osten DW (1988) Selection of optimal regression models via cross-validation. J Chemometr 2:39-48. doi:10.1002/cem.1180020106
19
Park ES, Cho BK (2014) Development of drought stress measurement method for red pepper leaves using hyperspectral short wave infrared imaging technique. Protected Hort Plant Fac 23:50-55 (in Korean). doi:10.12791/ksbec.2014.23.1.050
20
Santesteban LG, Di Gennaro SF, Herrero-Langreo A, Mranda C, Royo JB, Matese A (2017) High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agric Water Manag 183:49-59. doi:10.1016/j.agwat.2016.08.026
21
Yuan L, Huang Y, Loraamm RW, Nie C, Wang J, Zhang J (2014) Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects. Field Crops Res 156:199-207. doi:10.1016/j.fcr.2013.11.012
Information
  • Publisher :KOREAN SOCIETY FOR HORTICULTURAL SCIENCE
  • Publisher(Ko) :한국원예학회
  • Journal Title :Horticultural Science and Technology
  • Journal Title(Ko) :원예과학기술지
  • Volume : 36
  • No :6
  • Pages :831-840
  • Received Date : 2018-06-29
  • Revised Date : 2018-10-01
  • Accepted Date : 2018-09-08
  • DOI :https://doi.org/10.12972/kjhst.20180081
Journal Informaiton Horticultural Science and Technology Horticultural Science and Technology
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