Abstract
In the past few years, multifractal detrended fluctuation analysis (MF-DFA) method has been widely applied in the field of agricultural image processing. However, this image analysis involves a great deal of iterative processes and matrix calculations, which require massive computational capacity. In order to overcome this problem, we first develop an MF-DFA program that involves image preprocessing, image segmentation, local area accumulation matrix calculation, local area trend fitting, local area trend elimination, a global qth order fluctuation function, and the Hurst index. We then compare and analyze the MF-DFA module's performance characteristics and propose a parallel optimization scheme of the MF-DFA based on OpenMP. The results of our rigorous performance evaluation study results clearly demonstrate that our proposed parallel optimization scheme is a highly efficient method in extracting rape leaf image texture characteristics.
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