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Detecting Wheat Powdery Mildew and Predicting Grain Yield Using Unmanned Aerial Photography

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Academic year: 2020

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Figure

Table 1. Key information for aerial imagery collected in each season
Fig. 1. Coefficients of correlation of winter wheat powdery mildew index with imaging variables extracted from digital images at five flight altitudes over five seasons.photography, respectively; lgR, lgG, lgB, and lgI means the logarithms of the value of red, green, blue, and intensity, respectively; G/R, Gand ‘s,’ ‘4,’ ‘+,’ ‘×,’ ‘à’ represent the years 2010, 2011, 2012, 2013, and 2014, respectively; R, G, B, I, H, S means the value of red, green, blue, intensity, hue, and saturation acquired from aerial–R, and (G–R)/(G+R) are compositevariables that combine the values of red and green.
Fig. 2. Fitted random coefficient models relating lgR (an imaging variable extracted from unmanned aerial digital photography) to powdery mildew index on winter wheat at the sameflight altitude among different years (A), and in the same year among different flight altitudes (B).
Fig. 3. BLUPs (best linear unbiased predictors) of the slopes and intercepts describingthe relationship of winter wheat powdery mildew with lgR (an imaging variableextracted from unmanned aerial digital photography).
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