## 6 Conclusions  
In this paper, we propose a human-machine user interface for the agricultural domain, which first synthesizes the natural language commands entered by the user into a structured AOM statement (Action-Object-Member) through a new approach based on a dynamic tuple language framework to collect training data. Constructing NL2 AOM, i.e., NL command-AOM call pairs, for the HMI approach interface, the method achieves an overall average accuracy, precision, recall, and F-score value of 85.21%, 87.65%, 86.67%, and 87.23% for command parsing, respectively. Then, we propose an end-to-end framework to improve the Seq2 Seq model based on BERT, which uses a special mask mechanism to implement the sequence-to-sequence concept based on self-attention to obtain global information about the commands. It enables NL2 AOM to learn the semantic mapping from fuzzy and unstructured NL commands to formal and structured intermediate language AOM calls. Thus, a natural user interface with high scientific generalization capability and scalability is constructed. The experimental results show that the values of each evaluation metric when the method was tested were 78.71%, 79.21%, 78.98%, and 79.09%. The experimental surface shows that the method of this paper has good performance indexes in agricultural human-machine interaction, and the response time of human-machine interaction is relatively rapid.  
In this work, we are using the Chinese natural language commands dataset from an agricultural measurement and control environment; this leads to the limitation of our model to agricultural measurement and control environment for Chinese natural language commands. In the future, we will consider migrating our models to different domains. Examples are the smart home domain and the industrial domain, and we can try other languages, such as English and Spanish. We consider a knowledge graph perspective  
Figure 14: Mobile platform human–machine interaction interface in agriculture.  
to store data and make semantic associations more visible. The data collection method in this paper still has shortcomings, and the collection process relies more on the rule information of commands. In addition, the generation process uses the concept of sequence-to-sequence, but the same AOM statement may have multiple equivalent natural language command sequences, so training a sequence-to-sequence model is prone to the \"order problem\", and we will consider using reinforcement learning methods to overcome this problem. In conclusion, our approach provides a new way of thinking about natural human-machine interaction interfaces in agriculture, and we will further explore new areas of human-machine interactions in the future.  
Conceptualization, Y.Z. ([PERSON]) and H.W.; methodology, Y.Z. ([PERSON]); software, P.W.; validation, S.Y. and Z.X.; resources, Y.W.; data curation, P.W. and Z.X.; writing--original draft preparation, Y.Z. ([PERSON]); writing--review and editing, Y.Z. ([PERSON]) and S.Y.; project administration, Y.W.; funding acquisition, Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by Anhui agricultural ecological and environmental protection and quality safety industrial technology system grant number 22803029.  
Not applicable.  
We sincerely thank the anonymous reviewers for their hard work. This research was completed with support from the Beidou Precision Agricultural Information Engineering Laboratory of Anhui Province. We thank them for providing technical support on modern agricultural measurement and control systems.  
The authors declare no conflict of interest.