of Suitable Leaf for Nitrogen Diagnosis in Rice Based on Computer Vision
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    Abstract:

    Prior research indicated that leaves at different positions responds differentially to the spectral characteristics under different nitrogen rates. A method based on the computer vision technology was proposed, by comparing the spatial differences of color parameters which was captured from the scanned images of upper fully expanded leaves. The result illustrated that the diagnosis of rice based on the scanned image under different N rates is able to partly reflect the hyperspectral properties. And the B、b、b/(r+g)、b/r、b/g were selected as the optimum color parameters. The coefficient of variation (CV) of the color parameters is higher at low N condition than normal. Furthermore, CV decreases with the increased leaf position. Meanwhile, the difference of CV at different part of the leaf is not obviously. The preliminary research concluded that the third fully expanded leaf can be applied as the ideal indicator to quantify the different status of nitrogen.

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