Evaluation of different vegetation discriminator indices and image processing algorithms to estimate water productivity
A canopy cover percentage is considered one of the most important evaluation criteria for evaluating the simultaneous effects of impressive factors on water efficiency. Since digital cameras are developed and widely available, the use of discrimination indices in the visible spectrum is making it possible to calculate the leaf area index and chlorophyll content of vegetation covers. Therefore, in this study, the performance of five plant Vegetation Discrimination Indices (VIDs) and a variety of thresholding algorithms was compared in order to distinguish the sugar beet's vegetation cover from its background, among which two new indices were introduced. In comparison with the old VID of Excess Green minus excess Red (ExGR), using the new VID of Excess Green minus excess Blue (ExGB) and Riddler-Calvard's thresholding algorithm resulted in a 29.54 percent increase in vegetation cover segmentation accuracy. Following this step, we determined which function would best predictdry beet weight from vegetation cover percentage, and the power function did the best. In order to estimate the yield, the segmentation method based on Riddler-Calvard thresholding and the New Canopy Index of Vegetation Extraction (CIVEn) had an error of 12.09 Kg. With an error of 41.25 Kg, the segmentation method based on Otsu thresholding and ExGR index performed worst.
- حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران میشود.
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