A matching model for plant growth environment based on weighted multi-dimensional tree designed for big data

There are about 500,000 species of plants on the earth, and their growing conditions differ. Making good use of these data can help achieve high-quality and high-yield agriculture. However, until now there is not yet a satisfied method to match suitable plants growing conditions with natural environment factors that plants live by. Thus this paper innovatively proposes a solution to the problem making use of the huge database formed by a variety of natural environment and plants growing conditions adopting matching algorithm based on weighted multidimensional tree. First, an auxin model is constructed. Then, based on it a weighted multidimensional tree is built for search purpose. The weighted multidimensional tree is an M-tree, which regional search first locates a certain area through key auxins, and then realizes accurate matching by means of similarity matching. The analysis and simulation results show that the proposed model is superior in efficiency to KD- tree and SV3- tree in the big data environment. Thus the mode proposed is ideal for searching in big data environments.