Wheat coverage extraction based on improved salp swarm algorithm for optimizing K-Means

Abstract

Aiming at the problems of high dependence of the K-Means algorithm on the initial clustering center and local optimal stagnation, a wheat coverage extraction algorithm with optimized K-Means by an improved salp swarm algorithm was proposed. First, the wheat image was converted to HSV colour space; Then the improved salp swarm algorithm was used to find the global optimal value as the initial clustering center of K-Means algorithm; Afterwards, the K-Means algorithm was used for local optimization until the iteration was completed; Finally the segmented wheat image was output. In order to evaluate algorithm performance, ISSA and other intelligent optimization algorithms were compared and tested by using 12 benchmark functions. Meanwhile, the improved salp swarm optimization K-Means algorithm was applied to wheat coverage extraction. The results indicate that the optimization accuracy and convergence speed of ISSA algorithm are superior to other algorithms, and its robustness is also significantly improved. Compared with other algorithms, the wheat image segmentation by ISSA-K algorithm has clearer texture and better effect. At the same time, it has the advantage of being more efficient which can be used for wheat coverage extraction and has strong practicability.

Keywords

• image processing; k-means; improved salp swarm algorithm; hsv color space; image segmentation; wheat coverage extraction