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2019 Vol.37, Issue 6 Preview Page

Research Article

Near-Infrared Hyperspectral Imaging for Detection of Bacterial Fruit Blotch in Watermelon Seedlings
Ho Jun Lee1
,
Dong Hwa Jang1
,
Yong Hoon Lee2
,
Yong Hyeon Kim34*
1Department of Agricultural Machinery Engineering, Graduate School, Jeonbuk National University
2Division of Biotechnology, Jeonbuk National University
3Department of Bioindustrial Machinery Engineering, Jeonbuk National University
4Institute for Agricultural Machinery & ICT Convergence, Jeonbuk National University
* Corresponding Author
31 December 2019. pp. 719-732
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Abstract

This study was conducted to detect bacterial fruit blotch (BFB) in watermelon seedlings using near-infrared hyperspectral imaging (HSI) and to compare discrimination accuracies using a partial least squares-discriminant analysis (PLS-DA) model with two extraction methods, band ratio, and principal component analysis (PCA). Three concentrations (1 × 102, 104, and 106 cfu/mL) of Acidovorax avenae subsp. citrulli were inoculated on the first true leaves of watermelon seedlings. Hyperspectral images were captured for 100 samples at 1 - 5 days after inoculation. The mean spectra for watermelon leaves infected with BFB were higher than those for the healthy leaves in the near-infrared range. Mean spectra for samples increased with increasing days after inoculation. This result was distinctly observed at the higher concentration. Two wavelengths, 1,588 and 1,450 nm, were selected as significant bands for the extraction of diseased area based on statistical t-tests. BFB symptoms were observed even at 1 day after inoculation from hyperspectral images taken out by two extraction methods. A PLS-DA model with band ratio extraction revealed better classification accuracies to distinguish BFB in watermelon leaves. A PLS-DA plot showed distinct groupings of diseased and healthy leaves in the calibration and validation sets. Hyperspectral imaging of watermelon leaves infected with BFB were visualized by applying the PLS-DA model. Our results demonstrate that near-infrared HSI is a promising technique for the detection of BFB in watermelon leaves.

Keywords
classification accuracy
disease detection
partial least squares regression
preprocessing
spectral wavelengths

References
1
Deng S, Xu Y, Li X, He Y (2015) Moisture content prediction in tealeaf with near infrared hyperspectral imaging. Comput Electron Agric 118:38-46. doi:10.1016/j.compag.2015.08.014
10.1016/j.compag.2015.08.014
2
Dutta B, Scherm H, Gitaitis RD, Walcott RR (2012) Acidovorax citrulli seed inoculum load affects seedling transmission and spread of bacterial fruit blotch of watermelon under greenhouse conditions. Plant Disease 96:705-711. doi:10.1094/PDIS-04-11-0292
10.1094/PDIS-04-11-029230727513
3
ElMasry G, Wang N, ElSayed A, Ngadi M (2007) Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. J Food Eng 81:98-107. doi:10.1016/j.jfoodeng.2006.10.016
10.1016/j.jfoodeng.2006.10.016
4
Feng X, Zhao Y, Zhang C, Cheng P, He Y (2017) Discrimination of transgenic maize kernel using NIR hyperspectral imaging and multivariate data analysis. Sensors 17:1894-1907. doi:10.3390/s17081894
10.3390/s1708189428817075PMC5580036
5
Folch-Fortuny A, Prats-Montalban JM, Cubero S, Blasco J, Ferrer A (2016) VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits. Chemometr Intell Lab Syst 156:241-248. doi:10.1016/j.chemolab.2016.05.005
10.1016/j.chemolab.2016.05.005
6
Huang M, Kim MS, Delwiche SR, Chao K, Qin J, Mo C, Esquerre C, Zhu Q (2016) Quantitative analysis of melamine in milk powders using near-infrared hyperspectral imaging, and band ratio. J Food Eng 181:10-19. doi:10.1016/j.jfoodeng.2016.02.017
10.1016/j.jfoodeng.2016.02.017
7
Jang SH, Hwang YK, Lee HJ, Lee JS, Kim YH (2018) Selecting significant wavelengths to predict chlorophyll content of grafted cucumber seedlings using hyperspectral images. Korean J Remote Sens 34:681-692. doi:10.7780/kjrs.2018.34.4.10
8
Jang SH, Hwang YK, Lee HJ, Lee JS, Lee WS, Kim YH (2018) Shortwave infrared hyperspectral imaging can predict moisture content in grafted cucumber seedlings. Hortic Sci Technol 36:831-840. doi:10.12972/kjhst.20180081
10.12972/kjhst.20180081
9
Jiang H, Wang W, Zhuang H, Yoon S, Li Y, Yang Y (2018) Visible and near-infrared hyperspectral imaging for cooking loss classification of fresh broiler breast fillets. Appl Sci 8:256-270. doi:10.3390/app8020256
10.3390/app8020256
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
10.1016/j.compag.2010.09.008
11
Kandpal LM, Lee SD, Kim MS, Bae HJ, Cho BK (2015) Short wave infrared (SWIR) hyperspectral imaging technique for examination of aflatoxin B1 (AFB1) on corn kernels. Food Control 51:171-176. doi:10.1016/j.foodcont.2014.11.020
10.1016/j.foodcont.2014.11.020
12
Lee HS, Kim MS, Lim HS, Park ES, Lee WH, Cho BK (2016) Detection of cucumber green mottle mosaic virus-infected watermelon seeds using a near-infrared (NIR) hyperspectral imaging system: Application to seeds of the "Sambok Honey" cultivar. Biosystems Eng 148:138-147. doi:10.1016/j.biosystemseng.2016.05.014
10.1016/j.biosystemseng.2016.05.014
13
Lee HS, Kim MS, Song YR, Oh CS, Lim HS, Lee WH, Kang JS, Cho BK (2017) Non-destructive evaluation of bacteria-infected watermelon seeds using visible/near-infrared hyperspectral imaging. Sci Food Agric 97:1084-1092. doi:10.1002/jsfa.7832
10.1002/jsfa.783227264863
14
Lee LC, Liong CY, Jemain AA (2018) Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: A review of contemporary practice strategies and knowledge gaps. Analyst 143:3526-3539. doi:10.1039/C8AN00599K
10.1039/C8AN00599K29947623
15
Li B, Shi Y, Shan C, Zhou Q, Ibrahim M, Wang Y, Wu G, Li H, Xiea G, et al. (2013) Effect of chitosan solution on the inhibition of Acidovorax citrulli causing bacterial fruit blotch of watermelon. J Sci Food Agric 93:1010-1015. doi:10.1002/jsfa.5812
10.1002/jsfa.581223400827
16
Li J, Chena L, Huang W, Wang Q, Zhang B, Tian X, Fan S, Li B (2016) Multispectral detection of skin defects of bi-colored peaches based on vis-NIR hyperspectral imaging. Postharvest Biol Technol 112:121-133. doi:10.1016/j.postharvbio.2015.10.007
10.1016/j.postharvbio.2015.10.007
17
Lins EC, Belasque J, Marcassa LG (2009) Detection of citrus canker in citrus plants using laser induced fluorescence spectroscopy. Precis Agric 10:319-330. doi:10.1007/s11119-009-9124-2
10.1007/s11119-009-9124-2
18
López-Maestresalas A, Keresztes JC, Goodarzi M, Arazuri S, Jaren C, Saeys W (2016) Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging. Food Control 70:229-241. doi:10.1016/j.foodcont.2016.06.001
10.1016/j.foodcont.2016.06.001
19
Mahlein AK, Kuska MT, Thomas S, Bohnenkamp D, Alisaac E, Behmann J, Wahabzada M, Kersting K (2017) Plant disease detection by hyperspectral imaging: From the lab to the field. Adv Anim Biosci 8:238-243. doi:10.1017/S2040470017001248
10.1017/S2040470017001248
20
Martinelli F, Scalenghe R, Davino S, Panno S, Scuderi G, Ruisi P, Villa P, Stroppiana D, Boschetti M, et al. (2015) Advanced methods of plant disease detection. A review. Agron Sustain Dev 35:1-25. doi:10.1007/s13593-014-0246-1
10.1007/s13593-014-0246-1
21
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
10.1016/j.biosystemseng.2017.09.009
22
Mo CY, Kim MS, Kim GY, Cheong EJ, Yang JY, Lim, JG (2015) Detecting drought stress in soybean plants using hyperspectral fluorescence imaging. Korean J Biosyst Eng 40:335-344. doi:10.5307/JBE.2015.40.4.335
10.5307/JBE.2015.40.4.335
23
Nouri M, Gorretta N, Vaysse P, Giraud M, Germain C, Keresztes B, Roger JM (2018) Near infrared hyperspectral dataset of healthy and infected apple tree leaves images for the early detection of apple scab disease. Data Brief 16:967-971. doi:10.1016/j.dib.2017.12.043
10.1016/j.dib.2017.12.04329322077PMC5752091
24
Qin J, Chao K, Kim M S, Lu R, Burks TF (2013) Hyperspectral and multispectral imaging for evaluating food safety and quality. J Food Eng 118:157-171. doi:10.1016/j.jfoodeng.2013.04.001
10.1016/j.jfoodeng.2013.04.001
25
Rajendran DK, Park ES, Nagendran R, Hung NB, Cho BK, Kim KH, Lee YH (2016) Visual analysis for detection and quantification of Pseudomonas cichorii disease severity in tomato plants. Plant Pathol J 32:300-310. doi:10.5423/PPJ.OA.01.2016.0032
10.5423/PPJ.OA.01.2016.003227493605PMC4968640
26
Rodriguez-Pulido FJ, Barbin DF, Sun DW, Gordillo B, Lourdes Gonzalez-Miret M, Heredia FJ (2013) Grape seed characterization by NIR hyperspectral imaging. Postharvest Biol Technol 76:74-82. doi:10.1016/j.postharvbio.2012.09.007
10.1016/j.postharvbio.2012.09.007
27
Sankaran S, Mishra A, Ehsani R, Davis C (2010) A review of advanced techniques for detecting plant diseases. Comput Electron Agric 72:1-13. doi:10.1016/j.compag.2010.02.007
10.1016/j.compag.2010.02.007
28
Sevgi TK, Christian WH (2017) A review of mid-infrared and near-infrared imaging: Principles, concepts and applications in plant tissue analysis. Molecules 22:168-187. doi:10.3390/molecules22010168
10.3390/molecules2201016828117673PMC6155813
29
Siedliska A, Baranowski P, Zubik M, Mazurek W, Sosnowska B (2018) Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging. Postharvest Biol Technol 139:115-126. doi:10.1016/j.postharvbio.2018.01.018
10.1016/j.postharvbio.2018.01.018
30
Sun Y, Wang Y, Xiao H, Gu X, Pan L, Tu K (2017) Hyperspectral imaging detection of decayed honey peaches based on their chlorophyll content. Food Chem 235:194-202. doi:10.1016/j.foodchem.2017.05.064
10.1016/j.foodchem.2017.05.06428554626
31
Sun Y, Wei K, Liu Q, Pan L, Tu K (2018) Classification and discrimination of different fungal diseases of three infection levels on peaches using hyperspectral reflectance imaging analysis. Sensors 18:1295-1308. doi:10.3390/s18041295
10.3390/s1804129529690625PMC5948498
32
Walcott RR, Gitaitis RD, Castro AC (2003) Role of blossoms in watermelon seed infestation by Acidovorax avenae subsp. citrulli. Phytopathology 93:528-534. doi:10.1094/PHYTO.2003.93.5.528
10.1094/PHYTO.2003.93.5.52818942974
33
Wetterich CB, Neves RFO, Belasque J, Marcassa LG (2016) Detection of citrus canker and Huanglongbing using fluorescence imaging spectroscopy and support vector machine technique. Appl Opt 55:400-407. doi:10.1364/AO.55.000400
10.1364/AO.55.00040026835778
34
Willems A, Goor M, Thielemans S, GillisM, Kersters K, Deley J (1992) Transfer of several phytopathogenic Pseudomonas species to Acidovorax as Acidovorax avenae subsp. avenae subsp. nov., comb. nov., Acidovorax avenae subsp. citrulli, Acidovorax avenae subsp. cattleyae, and Acidovorax konjaci. Int J Syst Bacteriol 42:107-119. doi:10.1099/00207713-42-1-107
10.1099/00207713-42-1-1071371056
35
Yang C, Lee WS, Gader P (2014) Hyperspectral band selection for detecting different blueberry fruit maturity stages. Comput Electron Agric 109:23-31. doi:10.1016/j.compag.2014.08.009
10.1016/j.compag.2014.08.009
36
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
10.1016/j.fcr.2013.11.012
37
Zhang C, Guo C, Liu F, Kong W, He Y, Lou B (2016) Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine. J Food Eng 179:11-18. doi:10.1016/j.jfoodeng.2016.01.002
10.1016/j.jfoodeng.2016.01.002
38
Zhao Y, Li X, Yu K, Cheng F, He Y (2016) Hyperspectral imaging for determining pigment contents in cucumber leaves in response to angular leaf spot disease. Sci Rep 6:1-9. doi:10.1038/srep27790
10.1038/srep2779027283050PMC4901261
Information
  • Publisher :KOREAN SOCIETY FOR HORTICULTURAL SCIENCE
  • Publisher(Ko) :원예과학기술지
  • Journal Title :Horticultural Science and Technology
  • Journal Title(Ko) :원예과학기술지
  • Volume : 37
  • No :6
  • Pages :719-732
  • Received Date : 2019-08-24
  • Revised Date : 2019-09-07
  • Accepted Date : 2019-09-14
  • DOI :https://doi.org/10.7235/HORT.20190072
Journal Informaiton Horticultural Science and Technology Horticultural Science and Technology
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