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Fig. 4 | Plant Methods

Fig. 4

From: A comparison of ImageJ and machine learning based image analysis methods to measure cassava bacterial blight disease severity

Fig. 4

Support Vector Machine learning analysis of CBB water-soaked symptoms. A The variance explained by inoculation type (Xam WT or XamΔTAL20), DPI (4-, 6- and 9-), or the interaction between inoculation type and DPI for twelve machine learning generated measurements. Variances were determined by an ANOVA. B Total water-soaked area (pixels, y-axis) for sites infiltrated with each treatment (x-axis). Calculated p-values (Kolmogorov–Smirnov test) shown above the line in each plot. C Negative gray-scale mean (y-axis) of water-soaked lesions for Xam WT and XamΔTAL20 relative to mock inoculated spots (x-axis) within the same leaf. Calculated p-values (Kolmogorov–Smirnov test) shown above the line in each plot. In the machine learning analysis, the gray-scale mean was generated using the average mean of the “L” channel from the LAB color space

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Plant Methods

ISSN: 1746-4811