Segmentation of drone-acquired agricultural images using parallel algorithms
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Abstract
The images obtained with drones from a vertical perspective present important information on crops, which makes it possible to systematize various activities related to precision agriculture (AP). Sequential algorithms developed in traditional programming languages consume high time and hardware resources in processing large digital images. A parallel algorithm will be developed to segment images using the OpenMP library to reduce computation times. OpenMP is a library compatible with low-level programming languages that allow the implementation of parallel algorithms in C or C++ languages in Linux environments for multicore architectures. The sequential algorithm to implement it through parallelism was necessary to divide into several smaller tasks and run them on several cores available on multicore processors to improve processing speed. The metrics used (execution time, acceleration, efficiency, and computational cost) allowed evaluating of the algorithms' performance with images of different dimensions, obtaining favorable results that verify the improvement of the parallel algorithm. The parallelization of sequential algorithms shows a significant reduction in execution times (66.37%) with large images and (74.73%) with small images using the maximum number of cores (8)
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