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.
Highlights
Soybeans [Glycine max (L.)] are an important crop throughout the United States and the world
Understanding that relationship could lead to using spectral reflectance data as a tool to assess the severity of charcoal rot disease in soybean plants, reducing human bias associated with qualitative analysis of soybean plant tissue and cost and time issues connected with quantitative analysis
Significant (p < 0.05) correlations were observed between spectral reflectance values and microsclerotia content in the visible (400 - 670 nm), red-edge (680 - 760 nm), near-infrared (770 - 1290 nm), and shortwave-infrared (1300 - 2490 nm) regions of the light spectrum (Figure 1, Figure 2)
Summary
Soybeans [Glycine max (L.)] are an important crop throughout the United States and the world. Charcoal rot disease, caused by the fungus Macrophomina phaseolina (Tassi) Goid, ranked second to sixth among diseases suppressing soybean yield in the United States from 1996 to 2007 [1]. Qualitative and quantitative methods are used to detect charcoal rot disease and the severity of the infection in soybeans plants. Infected tissue has a grayish black color resembling a sprinkling of finely powdered charcoal, the name charcoal rot. The grayish black color is caused by the microsclerotia (hardened fungal survival bodies) content in the plant tissue. Even though visual ratings are used for many diseases including charcoal rot, they do not provide quantitative severity data. Alternative methods are needed to assess the severity of charcoal rot disease in soybean plant tissue
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