Precise Recommendation Method of Suitable Planting Areas of Maize Varieties Based on Knowledge Graph

文献类型： 外文期刊

第一作者： Zou, Yidong

作者： Zou, Yidong;Zhao, Chunjiang;Zou, Yidong;Pan, Shouhui;Yang, Feng;Zhang, Dongfeng;Han, Yanyun;Zhao, Xiangyu;Wang, Kaiyi;Zhao, Chunjiang;Zou, Yidong;Pan, Shouhui;Yang, Feng;Zhang, Dongfeng;Han, Yanyun;Zhao, Xiangyu;Wang, Kaiyi;Zhao, Chunjiang

作者机构：

关键词： maize varieties; knowledge graph; recommendation model; RippleNet; county-scale

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.6 ）

ISSN：

年卷期： 2023 年 13 卷 3 期

页码：

收录情况： SCI

摘要： The rapid increase in the number of new maize varieties and the intensification of market competition have raised the need to precisely promote new maize varieties to suitable planting areas and fully exploit the variety potential and win the market competition. This paper proposes a precise recommendation method for suitable planting areas of maize varieties based on a knowledge graph. The meteorology knowledge graph of maize ecological regions is constructed at county-scale and a RippleNet recommendation model is used to mine the potential spatial correlation of maize variety suitability in different meteorological environments. The county-scale precise recommendation for suitable planting areas is then realized. In total, 331 maize varieties and agricultural meteorological data of 59 experimental areas in the Huang-Huai-Hai ecological region are used for model training and testing (accuracy 76.3%). Through experimental comparison, the recommendation accuracy of this method is 24.3% higher than that of six traditional machine learning methods, 11.2% higher than that of graph attention networks, and 5.8% higher than that of graph convolution neural networks. This study provides a data-driven solution for the precise recommendation and market positioning of maize varieties, enhances the scientificity of variety recommendation and helps to fully exploit their planting potential.

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