RGB-based phenotyping of foliar disease severity under controlled conditions
نویسندگان
چکیده
Plant diseases induce visible modifications on leaves with the advance of infection and colonization, thus altering their spectral reflectance pattern. In this study, we evaluated region symptomatic five plant diseases: soybean rust (SBR), Calonectria leaf blight (CLB), wheat blast (WLB), Nicotiana tabacum-Xylella fastidiosa (NtXf), potato late (PLB). Ten indices were calculated from RGB channels (red, green, blue) images varying in percent severity, which obtained under controlled lighting homogeneous background. Image processing was automated for background removal pixel-level index calculation. Each averaged across pixels at level. We found high levels correlation between severity majority indices. The most highly associated overall hue index, atmospherically resistant normalized green red difference primary colors soil color index. leaf-level mean value each ten digital numbers gathered used to train boosted regression tree models predicting disease. Models SBR, CLB, WLB achieved prediction accuracies (>97%) testing dataset (20% original dataset). NtXf PLB had below 90%. performance model may be directly related symptomatology method can if are light background, but improvements should made using field- or greenhouse-acquired would require similar conditions.
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ژورنال
عنوان ژورنال: Tropical Plant Pathology
سال: 2021
ISSN: ['1983-2052', '1982-5676']
DOI: https://doi.org/10.1007/s40858-021-00448-y