A Method for Scientific Cultivation Analysis Based on Knowledge Graphs release_oxjnubkizfgfplxbuys4hmspbq

Abstract

The development of big data and artificial intelligence has improved the intelligence and informatization of scientific planting. A scientific cultivation analysis method based on knowledge graphs is proposed in this paper. First, the logical representation and the ontological representation are combined to realize the access of static cultivation information and dynamic cultivation experience, as well as their representation with graphs. Second, according to the characteristics of plant cultivation information, knowledge extraction is realized via relational computing. A relationship determination method based on the first derivative and a multi-level classification retrieval method based on a tree structure are proposed to extract cultivation experience from the experimental data. Then, multimedia technology is embedded in the RDF framework and implemented, which further realizes the display of decision suggestions after scientific cultivation information analysis. Finally, taking perennial flowers as an example, the realization and application performance of cultivation knowledge graphs are demonstrated.
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