A reasoning method for rice fertilization strategy based on spatiotemporal knowledge graph

The lack of multidimensional knowledge means that current reasoning methods for rice fertilization cannot make decisions accurate when faced with complex spatiotemporal conditions in general. Therefore, we propose a reasoning method for rice fertilization strategy based on spatiotemporal knowledge graph. First, we systematically organize multisource expert knowledge about rice fertilization, and construct an ontology for rice fertilization consisting of five core elements: rice variety, planting environment, nutrition diagnosis, fertilization schemes, and time and place. Spatiotemporal differences in rice fertilization knowledge are expressed by assessing spatiotemporal concepts, relations, and state instances. Second, we propose a reasoning method for rice fertilization strategy based on the constructed knowledge graph. This method leverages a certainty factor model for nutrition diagnosis and integrates case-based and rule-based reasoning to determine fertilization schemes for different stages. Finally, taking Pucheng County, China, as an example, knowledge from crowd-sensing data is obtained to construct a knowledge graph using the proposed method. The results demonstrate the method can support the expression and complex reasoning of rice fertilization decisions under different spatiotemporal conditions.