User interactivity can be enhanced in agricultural measurement and control systems, especially with the continuous advancements in natural language semantic processing. It is necessary to improve user-friendliness in control and query operations within the agricultural measurement and control field, in order to reduce the user operating costs. Firstly, a precise interface of human-computer interaction can be constructed to tailor for the agricultural domain, in order to efficiently translate the user's natural language input into understandable commands for the computer system. The current agricultural field has relied mainly on graphical user interfaces to meet human-computer interaction. But some limitations still remained over time, e.g., the high complexity of human-computer interaction and the low efficiency. Therefore, natural language interface (NLI) has been designed to establish the mapping between natural language from the nature of human-computer interaction. Agricultural measurement and control systems have been considered as the efficient strategy. Among them, the primary task of natural language understanding (NLU) is often used to transform the human language into computer-understandable structured expressions, in order to accurately capture the user's intention and semantics. Deep learning has been utilized to name entity recognition tasks in recent years. Relational components of sentences can be extracted to identify the sentence actions, and then incorporate the annotations of semantic roles, in order to understand the utterances for the computers. Entity recognition has distinctly realized the entity features in the specific domains. Commonly-named entities are usually characterized by fuzzy boundaries in the field of agricultural measurement and control systems. Some challenges remain in the quality of data and the accuracy of annotations, due to the relatively scarce data. It is important to directly apply to the agricultural measurement and control system. In this study, the agricultural measurement and control natural language interface (AMC-NLI) was presented to serve as the natural language interface for the agricultural measurement and control. The users were allowed to operate and control systems using natural language commands. These commands were interpreted using OPERATE, PLACE, and OBJECT attributes within the operate-place-object (OPO) ternary structure, and then transmitted to the gateways, nodes, or devices. Significant semantic information was previously lost using conventional methods when extracting entities from natural language commands, particularly when the commands contained multiple entities of the same type. Additionally, the entity order was confounded on the semantic relationships. A semantic parsing model called BERT-BiLSTM-ATT-CRF-OPO was proposed for the recognition tasks of the named entity in the command parsing of the measurement and control system. BERT pre-trained language models were utilized for the word embedding to enhance contextual understanding. The bidirectional long short-term memory networks (BiLSTM) were employed to capture the semantic features of long sentences and long-distance dependent information. An attention mechanism was incorporated to prioritize the features related to named entities for better local feature extraction. Conditional Random Field (CRF) was utilized to learn the labeling constraints and output globally optimal labeled sequences. The experimental results show that the BERT-BiLSTM-ATT-CRF-OPO model achieved a recognition accuracy of 92.13%, a recall of 93.12%, and an F1 score of 92.76% for the three types of entities. The improved model performed well in the AMC-NLI agricultural measurement and control command interaction, with the accuracy, precision, recall, F-value, and average maximum response time reaching 91.63%, 92.77%, 92.48%, 91.74%, and 2.45s, respectively. The human-computer interaction was enhanced in the agricultural measurement and control system, in order to improve the recognition accuracy of command entity. The finding can offer novel insights into Chinese command parsing, indicating the potential application of natural language processing in agriculture. A more user-friendly and efficient human-computer interaction was provided for future agricultural measurement and control systems.