All Issue

2019 Vol.37, Issue 6 Preview Page

Research Article


December 2019. pp. 719-732
Abstract


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