Abstract: With the rapid development of digital agriculture, crop named entity recognition, as the basis of knowledge graph construction in agriculture, is becoming an efficient crop recognition method. Since crop entity recognition presents complex structure, inconsistent entity designations, and multiple confounding factors, which seriously restrict the performance of entity recognition in crop domain, the paper proposes an entity recognition model based on pre-trained language models. BERT was used to encode words in text, bi-directional LSTM was used to obtain the context of each keyword in a sentence, and CRFs was used to capture the dependencies between words. The model was validated with the constructed crop named entity recognition dataset. The experiments demonstrate that the model can effectively recognize crop entities and outperforms the existing entity recognition models.