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

Yidong Zou, Shouhui Pan, Feng Yang, Dongfeng Zhang, Yanyun Han, Xiangyu Zhao, Kaiyi Wang and Chunjiang Zhao ()
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Yidong Zou: College of Information Engineering, Northwest Agriculture & Forestry University, Yangling 712100, China
Shouhui Pan: Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Feng Yang: Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Dongfeng Zhang: Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Yanyun Han: Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Xiangyu Zhao: Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Kaiyi Wang: Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Chunjiang Zhao: College of Information Engineering, Northwest Agriculture & Forestry University, Yangling 712100, China

Agriculture, 2023, vol. 13, issue 3, 1-19

Abstract: 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.

Keywords: maize varieties; knowledge graph; recommendation model; RippleNet; county-scale (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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