PurposeGrey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.Design/methodology/approachBased on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.FindingsThe results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.Originality/valueBy analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.
BP neural network (BPNN) is widely used due to its good generalization and robustness, but the model has the defect that it cannot automatically optimize the input variables. In response to this problem, this study uses the grey relational analysis method to rank the importance of input variables, obtains the key variables and the best BPNN model structure through multiple training and learning for the BPNN models, and proposes a variable optimization selection algorithm combining grey relational analysis and BP neural network. The predicted values from the metabolic GM (1, 1) model for key variables was used as input to the best BPNN model for prediction modeling, and a grey BP neural network model prediction model (GR-BPNN) was proposed. The long short-term memory neural network (LSTM), convolutional neural network (CNN), traditional BP neural network (BP), GM (1, N) model, and stepwise regression (SR) are also implemented as benchmark models to prove the superiority and applicability of the new model. Finally, the GR-BPNN forecasting model was applied to the grain yield forecast of the whole province and subregions for Henan Province. The forecasting results found that the growth rate of grain production in Henan Province slowed down and the center of gravity for grain production shifted northwards.
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