An Improved iTransformer with RevIN and SSA for Greenhouse Soil Temperature Prediction
In contemporary agricultural practices, greenhouses serve as a critical component of infrastructure, where soil temperature plays a vital role in enhancing pest management and regulating crop growth. However, achieving precise greenhouse environmental control continues to pose a significant challeng...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/223 |
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| author | Fahai Wang Yiqun Wang Wenbai Chen Chunjiang Zhao |
| author_facet | Fahai Wang Yiqun Wang Wenbai Chen Chunjiang Zhao |
| author_sort | Fahai Wang |
| collection | DOAJ |
| description | In contemporary agricultural practices, greenhouses serve as a critical component of infrastructure, where soil temperature plays a vital role in enhancing pest management and regulating crop growth. However, achieving precise greenhouse environmental control continues to pose a significant challenge. In this context, the present study proposes ReSSA-iTransformer, an advanced predictive model engineered to accurately forecast soil temperatures within greenhouses across diverse temporal scales, encompassing both long-term and short-term horizons. This model capitalizes on the iTransformer time-series forecasting framework and integrates Singular Spectrum Analysis (SSA) to decompose environmental variables, thereby augmenting the extraction of pivotal features, such as soil temperature. Furthermore, to mitigate the prevalent distribution shift issues inherent in time-series data, Reversible Instance Normalization (RevIN) is incorporated within the model architecture. ReSSA-iTransformer is adept at executing multi-step forecasts for both extended and immediate future intervals, thereby offering comprehensive predictive capabilities. Empirical evaluations substantiate that ReSSA-iTransformer surpasses conventional models, including LSTM, Informer, and Autoformer, across all assessed metrics. Specifically, it attained R<sup>2</sup> coefficients of 98.51%, 97.03%, 97.26%, and 94.83%, alongside MAE values of 0.271, 0.501, 0.648, and 1.633 for predictions at 3 h, 6 h, 24 h, and 48 h intervals, respectively. These results highlight the model’s superior accuracy and robustness. Ultimately, ReSSA-iTransformer not only provides dependable soil temperature forecasts but also delivers actionable insights, thereby facilitating enhanced greenhouse management practices. |
| format | Article |
| id | doaj-art-339e7723e900493daaf6ad4505399047 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-339e7723e900493daaf6ad45053990472025-01-24T13:17:14ZengMDPI AGAgronomy2073-43952025-01-0115122310.3390/agronomy15010223An Improved iTransformer with RevIN and SSA for Greenhouse Soil Temperature PredictionFahai Wang0Yiqun Wang1Wenbai Chen2Chunjiang Zhao3School of Automation, Beijing Information Science and Technology University, Beijing 100192, ChinaSchool of Automation, Beijing Information Science and Technology University, Beijing 100192, ChinaSchool of Automation, Beijing Information Science and Technology University, Beijing 100192, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaIn contemporary agricultural practices, greenhouses serve as a critical component of infrastructure, where soil temperature plays a vital role in enhancing pest management and regulating crop growth. However, achieving precise greenhouse environmental control continues to pose a significant challenge. In this context, the present study proposes ReSSA-iTransformer, an advanced predictive model engineered to accurately forecast soil temperatures within greenhouses across diverse temporal scales, encompassing both long-term and short-term horizons. This model capitalizes on the iTransformer time-series forecasting framework and integrates Singular Spectrum Analysis (SSA) to decompose environmental variables, thereby augmenting the extraction of pivotal features, such as soil temperature. Furthermore, to mitigate the prevalent distribution shift issues inherent in time-series data, Reversible Instance Normalization (RevIN) is incorporated within the model architecture. ReSSA-iTransformer is adept at executing multi-step forecasts for both extended and immediate future intervals, thereby offering comprehensive predictive capabilities. Empirical evaluations substantiate that ReSSA-iTransformer surpasses conventional models, including LSTM, Informer, and Autoformer, across all assessed metrics. Specifically, it attained R<sup>2</sup> coefficients of 98.51%, 97.03%, 97.26%, and 94.83%, alongside MAE values of 0.271, 0.501, 0.648, and 1.633 for predictions at 3 h, 6 h, 24 h, and 48 h intervals, respectively. These results highlight the model’s superior accuracy and robustness. Ultimately, ReSSA-iTransformer not only provides dependable soil temperature forecasts but also delivers actionable insights, thereby facilitating enhanced greenhouse management practices.https://www.mdpi.com/2073-4395/15/1/223time-series predictioniTransformersingular spectrum analysisreversible instance normalizationgreenhouse control |
| spellingShingle | Fahai Wang Yiqun Wang Wenbai Chen Chunjiang Zhao An Improved iTransformer with RevIN and SSA for Greenhouse Soil Temperature Prediction Agronomy time-series prediction iTransformer singular spectrum analysis reversible instance normalization greenhouse control |
| title | An Improved iTransformer with RevIN and SSA for Greenhouse Soil Temperature Prediction |
| title_full | An Improved iTransformer with RevIN and SSA for Greenhouse Soil Temperature Prediction |
| title_fullStr | An Improved iTransformer with RevIN and SSA for Greenhouse Soil Temperature Prediction |
| title_full_unstemmed | An Improved iTransformer with RevIN and SSA for Greenhouse Soil Temperature Prediction |
| title_short | An Improved iTransformer with RevIN and SSA for Greenhouse Soil Temperature Prediction |
| title_sort | improved itransformer with revin and ssa for greenhouse soil temperature prediction |
| topic | time-series prediction iTransformer singular spectrum analysis reversible instance normalization greenhouse control |
| url | https://www.mdpi.com/2073-4395/15/1/223 |
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