| Autor: |
Yongheng Zhang, Siyi Yao, Peng Wang, Hao Wu, Zhipeng Xu, Yongmei Wang, Youhua Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Applied Sciences, Vol 12, Iss 22, p 11830 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app122211830 |
| Popis: |
The human–machine interaction of existing agricultural measurement and control platforms lacks user-friendliness and requires manual operation by trained professionals. The recent development of natural language processing technology may bring some interesting changes. We propose a pipeline for building a natural language human–machine interaction interface to provide a better interaction for agricultural measurement and control platforms. Our construction process uses a new method of collecting training data based on the dynamic tuple language framework to synthesize natural language commands entered by the user into structured AOM statements (Action-Object-Member). To construct a mapping of the human–machine interface from natural language commands to AOM invocations, we propose an end-to-end framework that uses a special mask mechanism to improve the BERT-based Seq2Seq model to capture global sequence relations. Experimental results of data collection methods and NL2AOM demonstrate that our pipeline has good performance and a reasonable response time. Finally, we developed desktop and mobile platform applications based on the proposed model and used them in real agricultural scenarios. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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