## 1 Introduction  
Human-machine interaction [1] has attracted the attention of many researchers, and with the development of artificial intelligence (AI) technology and Internet of Things (IoT) technology, human-machine interaction is gradually being applied in the field of agriculture. The purpose of this technology is to achieve real-time information exchange between humans and machines, where workers can operate equipment remotely as well as query data in real time and return the results simply by voice or text input. Almost all existing human-machine interaction in agriculture is based on Graph User Interface (GUI) [2]. Staff with domain-specific knowledge are required to perform limited operations through the GUI, such as the deployment and commissioning of agricultural field equipment with the click of a button. However, if the control commands are too cumbersome, the time cost is too high, and the program of operation is fixed, the human-machine interaction is not friendly. The construction of a natural user interface (NUI) for agricultural measurement and control [3] is an effective means of solving this problem. NUI means that the user can interact with the machine naturally and directly, reducing the complexity of the interaction. In the web application domain [4, 5]; for example, web services hosted in the cloud (containing information queries on weather, sports, life, etc.) provide relevant data and services to usersthrough web program interfaces. In the mobile domain, relevant natural user interaction interfaces are also provided, with similar applications such as Apple's Siri [6], Microsoft's Cortana [7], and virtual assistants such as Huawei's Xiao E. In the database field, building natural language interfaces for databases [8] has also become a research hotspot; natural language interface of database (NLIDB) is a product that combines database technology and natural language-understanding technology. It aims to provide a natural language interface for various operations related to databases so that users can make query requests clearly and concisely. The usefulness of a virtual assistant depends heavily on its breadth, i.e., the number of services it supports [9]. The field-based human-machine interaction related to natural language interaction, such as agricultural measurement and control, is focused on a particular service. It needs to be optimized for this service to improve ht accuracy and avoid damage to equipment due to misuse. In related studies, keyword-based interactions are the most popular. [PERSON] et al. [10] built a virtual artificial intelligence assistant for the lab. The assistant looks for key phrases in voice commands that match a pre-built dictionary of device actions, but this approach is less targeted. [PERSON] et al. [11] constructed a virtual AI assistant for smart home environments, extracting linguistic elements such as location, environment, and movement from voice commands in a way that is somewhat goal-oriented. [PERSON] et al. [12] proposed the VISH grammar, which uses multiple rules to extract multiple types of entities from voice commands for analyzing the intent in voice commands and controlling target devices in smart home environments. A more targeted model is needed for applications of agricultural measurement and control systems. The core task of the natural user interface for the agricultural domain studied in this paper is to convert natural language commands entered by the user via speech or text into a machine language that can be executed by a computer system. The core problems are as follows:  
* How to construct a high-quality human-machine interaction dataset about the agricultural domain, i.e., a reasonable linguistic framework is needed to translate between the natural language commands entered by the user and the machine commands that can be understood by the computer system. An intermediate language framework needs to be found to structure the natural language for storage.