ORIGINAL ARTICLE
Detection of significant wavelengths for identifying and classifying Fusarium oxysporum during the incubation period and water stress in Solanum lycopersicum plants using reflectance spectroscopy
| 1 | Departamento de Ciencias Agronómicas, Universidad Nacional de Colombia, Facultad de Ciencias Agrícolas, Medellín, Colombia |
| 2 | Departamento de Geociencias y Medio Ambiente, Universidad Nacional de Colombia, Facultad de Minas, Medellín, Colombia |
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
CORRESPONDING AUTHOR
Juan Carlos Marín Ortiz
Departamento de Ciencias Agronómicas, Universidad Nacional de Colombia, Facultad de Ciencias Agrícolas, Medellín, Colombia
Departamento de Ciencias Agronómicas, Universidad Nacional de Colombia, Facultad de Ciencias Agrícolas, Medellín, Colombia
Submission date: 2019-01-03
Acceptance date: 2019-06-18
Online publication date: 2019-07-18
Journal of Plant Protection Research 2019;59(2):244–254
KEYWORDS
TOPICS
ABSTRACT
Spectroscopy has become one of the most used non-invasive methods to detect plant diseases
before symptoms are visible. In this study it was possible to characterize the spectral
variation in leaves of Solanum lycopersicum L. infected with Fusarium oxysporum during
the incubation period. It was also possible to identify the relevant specific wavelengths in
the range of 380–1000 nm that can be used as spectral signatures for the detection and
discrimination of vascular wilt in S. lycopersicum. It was observed that inoculated tomato
plants increased their reflectance in the visible range (Vis) and decreased slowly in the near
infrared range (NIR) measured during incubation, showing marked differences with plants
subjected to water stress in the Vis/NIR. Additionally, three ranges were found in the spectrum
related to infection by F. oxysporum (510–520 nm, 650–670 nm, 700–750 nm). Linear
discriminant models on spectral reflectance data were able to differentiate between tomato
varieties inoculated with F. oxysporum from healthy ones with accuracies higher than 70%
9 days after inoculation. The results showed the potential of reflectance spectroscopy to discriminate
plants inoculated with F. oxysporum from healthy ones as well as those subjected
to water stress in the incubation period of the disease.
CONFLICT OF INTEREST
The authors have declared that no conflict of interests exist.
REFERENCES (46)
1.
Abu-Khalaf N. 2015. Sensing tomato’s pathogen using Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA). Palestine Technical University Research Journal 3 (1): 12–22.
2.
Baayen R.P., O`Donnell K., Bonants P.J.M., Cigelnik E., Kroon L.P.N.M., Roebroeck J.A., Waalwijk C. 2000. Gene genealogies and AFLP analysis in the Fusarium oxysporum complex identify monophyletic and nonmonophyletic formae especiales causing wilt and rot disease. Phytopathology 90 (8): 891–900. DOI: https://doi.org/10.1155/2012/2....
3.
Berger S., Sinha A.K., Roitsch T. 2007. Plant physiology meets phytopathology: plant primary metabolism and plant–pathogen interactions. Journal of Experimental Botany 58 (15–16): 4019–4026. DOI: https://doi.org/10.1093/jxb/er....
4.
Berzaghi P., Riovanto R. 2010. Near infrared spectroscopy in animal science production: principles and applications. Italian Journal of Animal Science 8 (3): 39–62. DOI: https://doi.org/10.4081/ijas.2....
5.
Carter G.A. 1994. Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing 15 (3): 697–703. DOI: https://doi.org/10.1080/014311....
6.
Carter G.A., Knapp A.K. 2001. Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany 88 (4): 677–684. DOI: https://doi.org/10.2307/265706....
7.
Cregeen S., Radisek S., Mandelc S., Turk B., Stajner N., Jakse J., Javornik B. 2015. Different gene expressions of resistant and susceptible hop cultivars in response to infection with a highly aggressive strain of Verticillium alboatrum. Plant Molecular Biology Reporter 33 (3): 689–704. DOI: https://doi.org/10.1007/s11105....
8.
Demmig-Adams B., Gilmore A.M., Adams W.W. 1996. In vivo functions of carotenoids in higher plants. The FASEB Journal 10 (4): 403–412. DOI: https://doi.org/10.1096/fasebj....
9.
Filzmoser P., Maronna R., Werner M. 2007. Outlier identification in high dimensions. Computational Statistics and Data Analysis 52 (1): 299–308. DOI: https://doi.org/10.1016/j.csda....
10.
Fradin E.F., Thomma B. 2006. Physiology and molecular aspects of Verticillium wilt diseases caused by V-dahliae and V-alboatrum. Molecular Plant Pathology 7 (2): 71–86. DOI: https://doi.org/10.1111/j.1364....
11.
Genc L., Inalpulat M., Kizil U., Mirik M., Smith S., Mendes M. 2013. Determination of water stress with spectral reflectance on sweet corn (Zea mays L.) using classification tree (CT) analysis. Zemdirbyste-Agriculture 100 (1): 81–90. DOI: https://doi.org/10.13080/z-a.2....
12.
Gitelson A.A., Gritz Y., Merzlyak M. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology 160 (1): 271–282. DOI: https://doi.org/10.1078/0176-1....
13.
Gitelson A.A., Merzlyak M.N. 1996. Remote estimation of chlorophyll content in higher plant leaves. Journal of Plant Physiology 148 (2): 494–500. DOI: https://doi.org/10.1080/014311....
14.
Gitelson A.A., Merzlyak M.N. 1997. Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. International Journal of Remote Sensing 148 (3-4): 2691–2697. DOI: https://doi.org/10.1016/S0176-....
15.
Gitelson A.A., Zur Y., Chivkunova O.B., Merzlyak M.N. 2007. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology 75 (3): 272–281. DOI: https://doi.org/10.1562/0031-8....
16.
Gregory P.J., Ingram J.S.I., Andersson R., Betts R.A., Brovkin V., Chase T.N., Grace P.R., Gray A.J., Hamilton N., Hardy T.B., Howden S.M., Jenkins A., Meybeck M., Olsson M., Ortiz-Monasterio I., Palm C.A., Payne T.W., Rummukainena M., Schulze R.E., Thiema M., Valentin A., Wilkinson M.J. 2001. Environmental consequences of alternative practices for intensifying crop production. Agriculture, Ecosystems and Environment 88 (3): 279–290. DOI: https://doi.org/10.1016/S0167-....
17.
Hatfield L.J., Gitelson A.A., Schepers S.J., Walthall L.C. 2008. Application of spectral remote sensing for agronomic decisions. Agronomy Journal 100 (3): 117–131. DOI: https://doi.org/10.2134/agronj....
18.
Huang M.Y., Huang W.H., Liu L.Y., Huang Y.D., Wang J.H., Zhao C.H., Wan A.M. 2004. Spectral reflectance feature of winter wheat single leaf infested with stripe rust and severity level inversion. Transactions of the CSAE 20 (1): 176–180.
19.
Huang H., Yu H., Xu H., Ying Y. 2007. Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: A review. Journal of Food Engineering 87 (3): 303–313. DOI: https://doi.org/10.1016/j.jfoo....
20.
Hubert M., Rousseeuwn P., Branden K. 2005. ROBPCA: A new approach to robust principal component analysis. American Statistical Association and the American Society for Quality 47 (1): 64–79. DOI: https://doi.org/10.1198/004017....
21.
Jacquemoud S., Ustin A.L. 2001. Leaf Optical Properties. 4th ed, Cambridge University Press, Cambridge, UK, 154 pp.
22.
Jin X., Shi C., Yu Y., Yamada T., Sacks E.J. 2017. Determination of leaf water content by visible and near-infrared spectrometry and multivariate calibration in Miscanthus. Front Plant Science 8 (721): e28579992. DOI: https://doi.org/10.3389/fpls.2....
23.
Khaled A.Y., Aziz S.A., Bejo S.K., Nawi N.M., Seman I.A., Onwude D.I. 2017. Early detection of diseases in plant tissue using spectroscopy – applications and limitations. Applied Spectroscopy Reviews 53 (24): 36–64. DOI: https://doi.org/10.1080/057049....
24.
Kira K., Rendell L. 1992. The feature selection problem: traditional methods and a new algorithm. Proceedings of the 10th National Conference on Artificial Intelligence. San Jose, California, July 12–16, 1992. Available on: https://www.aaai.org/Papers/AA....
25.
Kononenko I., Simec E., Robnik-Sikonja M. 1997. Overcoming the myopia of induction learning algorithms with RELIEFF. Applied Intelligence 7 (1): 39–55.
26.
Larsolle A., Muhammed H.H. 2007. Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density. Precision Agriculture 8 (1–2): 37–47. DOI: https://doi.org/10.1007/s11119....
27.
Li M.Z. 2006. Spectroscopy Analysis Technology and Application. Science Press, Beijing, China. Mahlein A.K., Oerke E.C., Steiner U., Dehne H.W. 2012. Recent advances in sensing plant diseases for precision crop protection. European Journal of Plant Pathology 133 (1): 197–209. DOI: https://doi.org/10.1007/s10658....
28.
Mahlein A.K., Rumpf T., Welke P., Dehne H.W., Plümer L., Steiner U., Oerke E.C. 2013. Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment 128 (1): 21–30. DOI: https://doi.org/10.1016/j.rse.....
29.
Mahlein A.K., Steiner U., Dehne H.W., Oerke E.C. 2010. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agriculture 11 (4): 413-431. DOI: https://doi.org/10.1007/s11119....
30.
Marín-Ortiz J.C., Hoyos-Carvajal L.M., Botero-Fernández V. 2018. Detection of asymptomatic Solanum lycopersicum L. plants infected with Fusarium oxysporum using reflectance VIS spectroscopy. Colombian Journal of Horticultural Sciences 12 (2): 436–446. DOI: https://doi.org/10.17584/rcch2....
31.
Marín-Ortiz J.C., Gutierrez-Toro N., Botero-Fernández V., Hoyos-Carvajal L.M. 2019. Linking physiological parameters with visible/near-infrared leaf reflectance in incubation period of vascular wilt disease. Saudi Journal of Biological Sciences. (in press) DOI: https://doi.org/10.1016/j.sjbs....
32.
Merzlyak M.N., Solovchenko A.E., Gitelson A.A. 2003a. Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit. Postharvest Biology and Technology 27 (2): 197–211. DOI: https://doi.org/10.1016/S0925-....
33.
Merzlyak M.N., Gitelson A.A., Chivkunova O.B., Solovchenko A.E., Pogosyan S.I. 2003b. Application of reflectance spectroscopy for analysis of higher plant pigments. Russian Journal of Plant Physiology 50 (5): 704–710. DOI: https://doi.org/704-710.1021-4....
34.
Ortiz E., Hoyos-Carvajal L. 2016. Standard methods for inoculations of F. oxysporum and F. solani in Passiflora. African Journal of Agricultural Research 11 (17): 1569–1575. DOI: https://doi.org/10.5897/AJAR20....
35.
Reis A., Boiteux L. 2007. Outbreak of Fusarium oxysporum f. sp. lycopersici race 3 in commercial fresh-market tomato fields in Rio de Janeiro state, Brazil. Hoticultura Brasileira 25 (3): 451–454. DOI: http://dx.doi.org/10.1590/S010....
36.
Roberts A.D., Roth L.K., Perroy L.R. 2011. Hyperspectral indices vegetation indices. p. 309–328. In: “Hyperspectral Remote Sensing of Vegetation” (S.P. Thenkabail, B.J. Lyon, A. Huete, eds.), CRC Press, Boca Raton, Fl, USA.
37.
Robnik-ŠikonjaIgor M., Kononenko I. 2003. Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning 53 (1–2): 23–69. DOI: https://doi.org/10.1023/A:1025....
38.
Salman A., Lapidot I., Pomerantz A., Tsror L., Hammody Z., Moreh R., Huleihel M., Mordechai S. 2012. Detection of Fusarium oxysporum fungal isolates using ATR spectroscopy. Spectroscopy: An International Journal 27 (5–6): 551–556. DOI: https://doi.org/10.1155/2012/1....
39.
Sankaran S., Mishra A., Ehsani R., Davis C. 2010. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture 72 (1): 1–13. DOI: https://doi.org/10.1016/j.comp....
40.
Song S., Gong W., Zhu B., Huang Xi. 2011. Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance. ISPRS Journal of Photogrammetry and Remote Sensing 66 (5): 672–682. DOI: https://doi.org/10.1016/jisprs....
41.
Storti E., Bogani P., Bettini P., Bonzi Morassi L., Pellegrini M.G., Matteo M., Simeti C., Buiatti M. 1989. The pleiotropic phenotype of tomato cells selected for altered response to Fusarium oxysporum f. sp. lycopersici cell wall components. Theoretical and Applied Genetics 78 (5): 689–695. DOI: https://doi.org/10.1007/BF0026....
42.
Strzalka K., Kostecka-Gugala A., Latowski D. 2003. Carotenoids and environmental stress in plants: significance of carotenoid-mediated modulation of membrane physical properties. Russian Journal of Plant Physiology 50 (2): 168–173. DOI: https://doi.org/10.1023/A:1022....
43.
Teixeira C.A., Lopo M., Pascoa R., Lopes J. 2013. A review on the applications of portable near-infrared spectrometers in the agro-food industry. Applied Spectroscopy 67 (11): 1215–1233. DOI: https://doi.org/10.1366/13-072....
44.
Zhang M., Qin Z., Liu X., Ustin S.L. 2003. Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation 4 (4): 295–310. DOI: https://doi.org/10.1016/S0303-....
45.
Zhang Q., Li Q., Zhang G. 2012. Rapid determination of leaf water content using VIS/NIR spectroscopy analysis with wavelength selection. Spectroscopy: An International Journal 27 (2): 93–105. DOI: https://doi.org/10.1155/2012/2....
46.
Zur Y., Gitelson A.A., Chivkunova O.B., Merzlyak M.N. 2000. The spectral contribution of carotenoids to light absorption and reflectance in green leaves. p. 1–7. In: Proceedings of the 2nd International Conference Geospatial Information in Agriculture and Forestry. Buena Vista, Fl, USA.
