A Rapid Integration Method of Wild Ornamental Plant Resources Based on Improved Clustering Algorithm

Abstract

Wild ornamental plants are beneficial as well as dangerous for the environment. Because the introduction of attractive plants that are not suited to the local ecosystem can result in significant environmental damage, a quick integration strategy based on an enhanced clustering algorithm is proposed for wild ornamental plant resources. The technique is enhanced with density stratification by integrating the k-means distance measurement formula and establishing the objective function of clustering optimization. The cluster termination condition is controlled by the number of clusters k, and the wild plant data categories are continually merged. Uneven density distribution is used to deal with the wild plant distribution dataset. To obtain the distribution of wild ornamental plants in different regions, to estimate the optimal parameters of wild plant samples, to combine with maximum likelihood classification to obtain the plant flora differentiation degree, and to complete the resource integration, remote sensing images were used. Comprehensive survey and systematic sampling were used to conduct a complete survey of the protected area. The heat map of the plant size distribution shows that there is a clear negative correlation between the spatial scale difference and the overall density difference of the plant distribution, that is, it appears spatially. From the experimental analysis, it is observed that the high-density small-scale and low-density large-scale agglomeration distribution characteristics delay is 1.96 s.