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In the current development process of agricultural informatization, most sub-domains of agriculture face challenges such as dispersed data resources, difficulties in information integration, and low efficiency in knowledge utilization. As an emerging knowledge representation technology in recent years, knowledge graph has demonstrated powerful capabilities in semantic reasoning and data integration in specific agricultural domains. Simultaneously, it has enhanced the performance of some upper-level applications in agriculture. To systematically summarize recent research on the construction and application of knowledge graphs in the agricultural domain, the fundamentals of knowledge graphs and the process of agricultural knowledge graph construction were introduced. Furthermore, it summarized the key technologies involved in constructing an agricultural knowledge graph from four aspects: ontology modeling, information extraction, knowledge fusion, and knowledge processing. Subsequently, an overview of the current applications of agricultural knowledge graphs was provided and discussed in five aspects: information retrieval, question-answering systems, recommendation systems, expert diagnostic systems, and crop prediction. In conclusion, the research status of agricultural knowledge graphs was summarized and it was suggested that future research in agricultural knowledge graphs should explore areas such as multimodal knowledge reasoning, timely knowledge updating, multilingual knowledge queries, cross-domain data fusion, and sub-domain knowledge graph construction.