Temperature and relative humidity prediction in South China greenhouse based on machine learning
Abstract Prediction of the greenhouse temperature and relative humidity is very important, which can forecast the environment parameters for manual intervention in advance. However, temperature and relative humidity prediction systems face two critical limitations: inconsistent temporal resolution i...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08964-6 |
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| author | Xinyu Wei Yizhi Luo Xingxing Zhou Junhong Zhao Huazhong Lu Jie Li Jinrong Zheng Bin Li |
| author_facet | Xinyu Wei Yizhi Luo Xingxing Zhou Junhong Zhao Huazhong Lu Jie Li Jinrong Zheng Bin Li |
| author_sort | Xinyu Wei |
| collection | DOAJ |
| description | Abstract Prediction of the greenhouse temperature and relative humidity is very important, which can forecast the environment parameters for manual intervention in advance. However, temperature and relative humidity prediction systems face two critical limitations: inconsistent temporal resolution in data acquisition and the absence of standardized protocols for environmental data collection, which collectively lead to non-uniform control strategies that compromise system interoperability in agricultural applications. This research predicted the temperature and relative humidity with different time interval in South China greenhouse by the model of BPPSO, LSSVM and RBF, which has proved their superiority in temperature and relative humidity prediction. The results showed that the R2 of temperature and relative humidity increase gradually with the decrease of the time interval, and the time interval of 15 min got the maximum value. The R2 of the temperature predicted by three models were 0.923, 0.923,0.912, and the R2 of the relative humidity were 0.948,0.952, and 0.948, respectively. The prediction accuracy of relative humidity was higher than that of temperature. All three models could be used to predict temperature and relative humidity in greenhouses in South China, among which LSSVM had higher R2 than the other two models. When the time interval was 15 min, the MAE, MAPE and RMSE of temperature were 0.574, 1.941 and 0.867, respectively, while the relative humidity of that were 2.747, 3.383 and 3.907, respectively. It concluded that the LSSVM model with time interval of 15 min was suitable to predict the temperature and relative humidity in south China greenhouse. This study provides reference for early intervention of greenhouse temperature and relative humidity management. |
| format | Article |
| id | doaj-art-41cb28529ae642baac63cd6ad03ebdf0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-41cb28529ae642baac63cd6ad03ebdf02025-08-20T03:46:03ZengNature PortfolioScientific Reports2045-23222025-07-0115111210.1038/s41598-025-08964-6Temperature and relative humidity prediction in South China greenhouse based on machine learningXinyu Wei0Yizhi Luo1Xingxing Zhou2Junhong Zhao3Huazhong Lu4Jie Li5Jinrong Zheng6Bin Li7Institute of Facility Agriculture, Guangdong Academy of Agricultural SciencesInstitute of Facility Agriculture, Guangdong Academy of Agricultural SciencesInstitute of Facility Agriculture, Guangdong Academy of Agricultural SciencesInstitute of Facility Agriculture, Guangdong Academy of Agricultural SciencesGuangdong Academy of Agricultural SciencesEnvironmental Horticulture Research Institute, Guangdong Academy of Agricultural SciencesInstitute of Facility Agriculture, Guangdong Academy of Agricultural SciencesInstitute of Facility Agriculture, Guangdong Academy of Agricultural SciencesAbstract Prediction of the greenhouse temperature and relative humidity is very important, which can forecast the environment parameters for manual intervention in advance. However, temperature and relative humidity prediction systems face two critical limitations: inconsistent temporal resolution in data acquisition and the absence of standardized protocols for environmental data collection, which collectively lead to non-uniform control strategies that compromise system interoperability in agricultural applications. This research predicted the temperature and relative humidity with different time interval in South China greenhouse by the model of BPPSO, LSSVM and RBF, which has proved their superiority in temperature and relative humidity prediction. The results showed that the R2 of temperature and relative humidity increase gradually with the decrease of the time interval, and the time interval of 15 min got the maximum value. The R2 of the temperature predicted by three models were 0.923, 0.923,0.912, and the R2 of the relative humidity were 0.948,0.952, and 0.948, respectively. The prediction accuracy of relative humidity was higher than that of temperature. All three models could be used to predict temperature and relative humidity in greenhouses in South China, among which LSSVM had higher R2 than the other two models. When the time interval was 15 min, the MAE, MAPE and RMSE of temperature were 0.574, 1.941 and 0.867, respectively, while the relative humidity of that were 2.747, 3.383 and 3.907, respectively. It concluded that the LSSVM model with time interval of 15 min was suitable to predict the temperature and relative humidity in south China greenhouse. This study provides reference for early intervention of greenhouse temperature and relative humidity management.https://doi.org/10.1038/s41598-025-08964-6TemperatureRelative humidityPredictionSouth ChinaGreenhouseMachine learning |
| spellingShingle | Xinyu Wei Yizhi Luo Xingxing Zhou Junhong Zhao Huazhong Lu Jie Li Jinrong Zheng Bin Li Temperature and relative humidity prediction in South China greenhouse based on machine learning Scientific Reports Temperature Relative humidity Prediction South China Greenhouse Machine learning |
| title | Temperature and relative humidity prediction in South China greenhouse based on machine learning |
| title_full | Temperature and relative humidity prediction in South China greenhouse based on machine learning |
| title_fullStr | Temperature and relative humidity prediction in South China greenhouse based on machine learning |
| title_full_unstemmed | Temperature and relative humidity prediction in South China greenhouse based on machine learning |
| title_short | Temperature and relative humidity prediction in South China greenhouse based on machine learning |
| title_sort | temperature and relative humidity prediction in south china greenhouse based on machine learning |
| topic | Temperature Relative humidity Prediction South China Greenhouse Machine learning |
| url | https://doi.org/10.1038/s41598-025-08964-6 |
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