Automated analysis and textual summarization of time-varying references in advanced greenhouse climate control
The growing need for energy-efficient and sustainable crop production has made advanced control systems, such as Model Predictive Control (MPC), essential in greenhouse farming. MPC is an optimization-based control strategy that uses mathematical models and weather forecast data to regulate greenhou...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Agronomy |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fagro.2025.1536998/full |
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| author | Ramesh Arvind Naagarajan Kiran Kumar Sathyanarayanan Nadja Bauer Stefan Streif Stefan Streif |
| author_facet | Ramesh Arvind Naagarajan Kiran Kumar Sathyanarayanan Nadja Bauer Stefan Streif Stefan Streif |
| author_sort | Ramesh Arvind Naagarajan |
| collection | DOAJ |
| description | The growing need for energy-efficient and sustainable crop production has made advanced control systems, such as Model Predictive Control (MPC), essential in greenhouse farming. MPC is an optimization-based control strategy that uses mathematical models and weather forecast data to regulate greenhouse climates effectively. This technique generates time-varying climate reference trajectories, which are sent to the local process computer to control the corresponding climate parameter or equipment. While MPC and artificial intelligence-based techniques are becoming more common in advanced agricultural setups, their widespread adoption remains limited. Potential reasons are the lack of transparency and the understandability of the control algorithms. This study introduces a language-based support system to improve the usability of advanced control strategies like MPC. The system segments time-series data using the change point detection method to identify significant changes. The identified trend information is converted into detailed textual descriptions using the natural language generation technique. These descriptions are refined into user-friendly summaries with the assistance of a pretrained large language model. The results demonstrate that this support system can improve the accessibility and usability of advanced control strategies like MPC, making them more practical for greenhouse growers. |
| format | Article |
| id | doaj-art-5b2e37e2a3304245b32bee6e4bfebc5c |
| institution | DOAJ |
| issn | 2673-3218 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Agronomy |
| spelling | doaj-art-5b2e37e2a3304245b32bee6e4bfebc5c2025-08-20T02:40:42ZengFrontiers Media S.A.Frontiers in Agronomy2673-32182025-03-01710.3389/fagro.2025.15369981536998Automated analysis and textual summarization of time-varying references in advanced greenhouse climate controlRamesh Arvind Naagarajan0Kiran Kumar Sathyanarayanan1Nadja Bauer2Stefan Streif3Stefan Streif4Automatic Control and System Dynamics, Chemnitz University of Technology, Chemnitz, GermanyAutomatic Control and System Dynamics, Chemnitz University of Technology, Chemnitz, GermanyDepartment of Computer Science, Dortmund University of Applied Sciences and Arts, Dortmund, GermanyAutomatic Control and System Dynamics, Chemnitz University of Technology, Chemnitz, GermanyDepartment of Bioresources, Fraunhofer Institute for Molecular Biology and Applied Ecology, Giessen, GermanyThe growing need for energy-efficient and sustainable crop production has made advanced control systems, such as Model Predictive Control (MPC), essential in greenhouse farming. MPC is an optimization-based control strategy that uses mathematical models and weather forecast data to regulate greenhouse climates effectively. This technique generates time-varying climate reference trajectories, which are sent to the local process computer to control the corresponding climate parameter or equipment. While MPC and artificial intelligence-based techniques are becoming more common in advanced agricultural setups, their widespread adoption remains limited. Potential reasons are the lack of transparency and the understandability of the control algorithms. This study introduces a language-based support system to improve the usability of advanced control strategies like MPC. The system segments time-series data using the change point detection method to identify significant changes. The identified trend information is converted into detailed textual descriptions using the natural language generation technique. These descriptions are refined into user-friendly summaries with the assistance of a pretrained large language model. The results demonstrate that this support system can improve the accessibility and usability of advanced control strategies like MPC, making them more practical for greenhouse growers.https://www.frontiersin.org/articles/10.3389/fagro.2025.1536998/fulllarge language modelsmodel predictive controlnatural language generationprompt engineeringtime-series to text |
| spellingShingle | Ramesh Arvind Naagarajan Kiran Kumar Sathyanarayanan Nadja Bauer Stefan Streif Stefan Streif Automated analysis and textual summarization of time-varying references in advanced greenhouse climate control Frontiers in Agronomy large language models model predictive control natural language generation prompt engineering time-series to text |
| title | Automated analysis and textual summarization of time-varying references in advanced greenhouse climate control |
| title_full | Automated analysis and textual summarization of time-varying references in advanced greenhouse climate control |
| title_fullStr | Automated analysis and textual summarization of time-varying references in advanced greenhouse climate control |
| title_full_unstemmed | Automated analysis and textual summarization of time-varying references in advanced greenhouse climate control |
| title_short | Automated analysis and textual summarization of time-varying references in advanced greenhouse climate control |
| title_sort | automated analysis and textual summarization of time varying references in advanced greenhouse climate control |
| topic | large language models model predictive control natural language generation prompt engineering time-series to text |
| url | https://www.frontiersin.org/articles/10.3389/fagro.2025.1536998/full |
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