Share This Article:

Relationships between Microsclerotia Content and Hyperspectral Reflectance Data in Soybean Tissue Infected by Macrophomina phaseolina

Abstract Full-Text HTML XML Download Download as PDF (Size:2847KB) PP. 3737-3744
DOI: 10.4236/ajps.2014.525390    3,440 Downloads   3,894 Views   Citations

ABSTRACT

Alternative methods are needed to assess the severity of charcoal rot disease [Macrophomina phaseolina (Tassi) Goid] in soybean [Glycine max (L.)] plant tissue. The objective of this study was to define the relationship between light reflectance properties and microsclerotia content of soybean stem and root tissue. Understanding that relationship could lead to using spectral reflectance data as a tool to assess the severity of charcoal rot disease in soybean plants, thus reducing human bias associated with qualitative analysis of soybean plant tissue and cost and time issues connected with quantitative analysis. Hyperspectral reflectance measurements (400-2490 nm) were obtained with a non-imaging spectroradiometer of non-diseased and charcoal rot diseased ground stem and root tissue samples of six soybean genotypes (“Clark”, “LD00-3309”, “LG03- 4561-14”, “LG03-4561-19”, “Saline”, and “Y227-1”). Relationships between the reflectance measurements and tissue microsclerotia content were evaluated with Spearman correlation (rs) analysis (p < 0.05). Moderate (rs = ±0.40 to ±0.59), strong (rs = ±0.60 to ±0.79), and very strong (rs = ±0.80 to ±1.00) negative and positive statistically significant (p < 0.05) monotonic relationships were observed between tissue spectral reflectance values and tissue microsclerotia content. Near-infrared and shortwave-infrared wavelengths had the best relationships with microsclerotia content in the ground tissue samples, with consistent results obtained with near-infrared wavelengths in that decreases in near-infrared spectral reflectance values were associated with increases in microsclerotia content in the stem and root tissue of the soybean plants. The findings of this study provided evidence that relationships exist between tissue spectral reflectance and tissue microsclerotia content of soybean plants, supporting spectral reflectance data as a means for assessing variation of microsclerotia content in soybean plants. Future research should focus on the modelling capabilities of the selected wavelengths and on the feasibility of using these wavelengths in machine learning algorithms to differentiate non-diseased from charcoal rot diseased tissue.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Fletcher, R. , Smith, J. , Mengistu, A. and Ray, J. (2014) Relationships between Microsclerotia Content and Hyperspectral Reflectance Data in Soybean Tissue Infected by Macrophomina phaseolina. American Journal of Plant Sciences, 5, 3737-3744. doi: 10.4236/ajps.2014.525390.

References

[1] Wrather, J.A. and Koenning, S.R. (2009) Effects of Diseases on Soybean Yields in the United States 1996 to 2007. Plant Health Progress. http://dx.doi.org/10.1094/PHP-2009-0401-01-RS
[2] Westphal, A., Abney, T.S. and Shaner, G. (2006) Diseases of Soybean: Charcoal Rot. Purdue Extension, BP-42-W. https://www.extension.purdue.edu/extmedia/BP/BP-42-W.pdf
[3] Bissonnette, S. (2012) Charcoal Rot Is a Hot Disease in Soybeans.
http://agfax.com/2012/08/13/Illinois-charcoal-rot-a-hot-disease-in-soybeans/
[4] Mengistu, A., Ray, J.D., Smith, J.R. and Paris, R.L. (2007) Charcoal Rot Disease Assessment of Soybean Genotypes using a Colony Forming Unit Index. Crop Science, 47, 2453-2461.
http://dx.doi.org/10.2135/cropsci2007.04.0186
[5] Smith, G.S. and Wyllie, T.D. (1999) Charcoal Rot. In: Hartman, G.L., Sinclair, J.B. and Rupe, J.C., Eds., Compendium of Soybean Diseases, 4th Edition, American Phytopathological Society, St. Paul, 29-31.
[6] Odvody, G.N. and Dunkle, L.D. (1979) Charcoal Stalk Rot of Sorghum: Effect of Environment on Host-Parasite Relations. Phytopathology, 69, 250-254. http://dx.doi.org/10.1094/Phyto-69-250
[7] Paris, R.L., Mengistu, A., Tyler, J.M. and Smith, J.R. (2006) Registration of Soybean Germplasm Line DT97-4290 with Moderate Resistance to Charcoal Rot. Crop Science, 46, 2324-2325.
http://dx.doi.org/10.2135/cropsci2005.09.0297
[8] Sarr, M.P., Ndiaye, M., Groenewald, J.Z. and Crous, P.W. (2014) Genetic Diversity in Macrophomina phaseolina, the Causal Agent of Charcoal Rot. Phytopathologia Mediterranea, 53, 250-268.
[9] Smith, G.S. and Carvil, O.N. (1997) Field Screening of Commercial and Experimental Soybean Cultivars for Their Reaction to Macrophomina phaseolina. Plant Disease, 81, 363-368.
http://dx.doi.org/10.1094/PDIS.1997.81.4.363
[10] Delalieux, S., van Aardt, J., Keulemans, W. and Coppin, P. (2007) Detection of Biotic Stress (Venturia inaequalis) in Apple Trees using Hyperspectral Data: Nonparametric Statistical Approaches and Physiological Implications. European Journal of Agronomy, 27, 130-143.
http://dx.doi.org/10.1016/j.eja.2007.02.005
[11] Mahlein, A.K., Steiner, U., Dehne H.W. and Oerke, E.C. (2010) Spectral Signatures of Sugar Beet Leaves for the Detection and Differentiation of Diseases. Precision Agriculture, 11, 413-431.
http://dx.doi.org/10.1007/s11119-010-9180-7
[12] Mahlein, A.K., Steiner, U., Hillnhütter, C., Dehne, H.W. and Oerke E.C. (2012) Hyperspectral Imaging for Small-Scale Analysis of Symptoms Caused by Different Sugar Beet Diseases. Plant Methods, 8, 3. http://dx.doi.org/10.1186/1746-4811-8-3
[13] Ray, S.S., Jain, N., Arora, R.K., Chavan, S. and Panigrahy, S. (2011) Utility of Hyperspectral Data for Potato Late Blight Disease Detection. Journal of the Indian Society of Remote Sensing, 39, 161-169. http://dx.doi.org/10.1007/s12524-011-0094-2
[14] Rumpf, T., Mahlein, A.K., Steiner, U., Oerke, E.C., Dehne, H.W. and Plümer, L. (2010) Early Detection and Classification of Plant Diseases with Support Vector Machines Based on Hyperspectral Reflectance. Computers and Electronics in Agriculture, 74, 91-99. http://dx.doi.org/10.1016/j.compag.2010.06.009
[15] Sankaran, S., Mishra, A., Ehsani, R. and Davis, C. (2010) A Review of Advanced Techniques for Detecting Plant Diseases. Computers and Electronics in Agriculture, 72, 1-13.
http://dx.doi.org/10.1016/j.compag.2010.02.007
[16] Naidu, R.A., Perry, E.M., Pierceb, F.J. and Mekuria, T. (2009) The Potential of Spectral Reflectance Technique for the Detection of Grapevine Leafroll-Associated Virus-3 in Two Red-Berried Wine Grape Cultivars. Computers and Electronics in Agriculture, 66, 38-45.
http://dx.doi.org/10.1016/j.compag.2008.11.007
[17] Johnson, H.W. (1958) Registration of Soybean Varieties, VI. Agronomy Journal, 50, 690-691.
http://dx.doi.org/10.2134/agronj1958.00021962005000110016x
[18] Owen, P.A., Nickell, C.D., Noel, G.R., Thomas, D.J. and Frey, K. (1994) Registration of ‘Saline’ Soybean. Crop Science, 34, 1689. http://dx.doi.org/10.2135/cropsci1994.0011183X003400060051x
[19] Diers, B.W., Cary, T.R., Thomas, D.J. and Nickell, C.D. (2006) Registration of “LD00-3309” Soybean. Crop Science, 46, 1384. http://dx.doi.org/10.2135/cropsci2005.06.0164
[20] Mengistu, A., Ray, J.D., Smith, J.R. and Boykin, D.L. (2014) Maturity Effects on Colony-Forming Units of Macrophomina phaseolina Infection as Measured Using Near-Isogenic Lines of Soybeans. Journal of Crop Improvement, 28, 38-56. http://dx.doi.org/10.1080/15427528.2013.858284
[21] Field, A., Miles, J. and Field, Z. (2012) Discovering Statistics Using R. Sage Publication Ltd., London.
[22] Sheskin, D.J. (2007) Spearman’s Rank-Order Correlation Coefficient. In: Sheskin, D.J., Ed., Handbook of Parametric and Nonparametric Statistical Procedures, 4th Edition, Chapman & Hall/CRC, Boca Raton, 1353-1370.
[23] McKillup, S. (2005) Statistics Explained an Introductory Guide for Life Scientists. Cambridge University Press, Cambridge. http://dx.doi.org/10.1017/CBO9780511815935
[24] Bálint, J., Balázs, V.N. and Fail, J. (2013) Correlations between Colonization of Onion Thrips and Leaf Reflectance Measures across Six Cabbage Varieties. PLoS ONE, 8, e73848.
http://dx.doi.org/10.1371/journal.pone.0073848
[25] Stein, B.R., Thomas, V.A., Lorentz, L.J. and Strahm, B.D. (2014) Predicting Macronutrient Concentrations from Loblolly Pine Leaf Reflectance across Local and Regional Scales. GI Science and Remote Sensing, 51, 269-287.
[26] R Core Team (2013) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org/
[27] Wickham, H. (2009) GGplot2: Elegant Graphics for Data Analysis. Springer, New York.
http://dx.doi.org/10.1007/978 -0-387-98141-3
[28] Grosjean, P. and Ibanez, F. (2014) Pastecs: Package for Analysis of Space-Time Ecological Series. R Package Version 1.3-18. http://CRAN.R-project.org/package=pastecs
[29] Revelle, W. (2013) Psych: Procedures for Personality and Psychological Research. Northwestern University, Evanston. http://CRAN.R-project.org/package=psych
[30] Evans, J.D. (1996) Straightforward Statistics for the Behavioral Sciences. Brooks/Cole Publishing, Pacific Grove.
[31] Schimleck, L., Wright, P., Michell, A. and Wallis, A. (1997) Near Infrared Spectra and Chemical Compositions of E. globules and E. nitens Plantation Woods. Appita Journal, 50, 40-46.
[32] Kelley, S.S., Rials, T.G., Snell, R., Groom, L.H. and Sluiter, A. (2004) Use of Near Infrared Spectroscopy to Measure the Chemical and Mechanical Properties of Solid Wood. Wood Science Technology, 38, 257-276. http://dx.doi.org/10.1007/s00226-003-0213-5
[33] Peltier, A.J., Hatfield, R.D. and Grau, C.R. (2009) Soybean Stem Lignin Concentration Relates to Resistance to Sclerotinia sclerotiorum. Plant Disease, 93, 149-154. http://dx.doi.org/10.1094/PDIS-93-2-0149

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.