Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear Regression
Scientific management and environmental regulation of facility strawberries depends on the level of accurate prediction and forecasting of low temperature freezes in plastic greenhouses during winter and spring strawberry cultivation. Accurate identification of potential factors affecting layer-by-l...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/3/709 |
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| author | Xuelin Wang Qinqin Huang Dong Wu Jinhua Xie Ming Cao Jun Liu |
| author_facet | Xuelin Wang Qinqin Huang Dong Wu Jinhua Xie Ming Cao Jun Liu |
| author_sort | Xuelin Wang |
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| description | Scientific management and environmental regulation of facility strawberries depends on the level of accurate prediction and forecasting of low temperature freezes in plastic greenhouses during winter and spring strawberry cultivation. Accurate identification of potential factors affecting layer-by-layer minimum temperatures in plastic greenhouses and selection of optimal forecasting methods are important for safe strawberry production. However, the identification of important drivers of minimum temperatures in plastic greenhouses and the prediction of potential drivers of use are still unclear. In this study, we used Classification and Regression Tree (CART) to identify the importance of the potential factors affecting the minimum temperatures at different depths and different heights of plastic greenhouses. Random forest (RF), back-propagation (BP), and multiple linear regression (MLR) were used to establish the minimum temperature prediction models for plastic greenhouses at different depths and heights, respectively. The results showed that T<sub>smin10</sub>, T<sub>smin25</sub>, T<sub>amin150</sub>, T<sub>amin320</sub>, and T<sub>amin150</sub> were the most important variables explaining the changes in minimum temperatures at heights T<sub>smin25</sub>, T<sub>smin10</sub>, T<sub>smin2</sub>, T<sub>amin150</sub>, and T<sub>amin320</sub> respectively. RF, BP performed much better than MLR, as it showed much lower error indices (AE and RMSE) and higher R<sup>2</sup> than MLR. The superiority of RF and BP in predicting minimum temperatures is related to their ability to deal with non-linear and hierarchical relationships between minimum temperatures and predictors. The low-temperature frost protection and fine management of strawberries in the Changfeng area can be related to the prediction method of minimum temperature in plastic greenhouses constructed in this study. |
| format | Article |
| id | doaj-art-db44706cfbce411abd25de968a87a319 |
| institution | DOAJ |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Agronomy |
| spelling | doaj-art-db44706cfbce411abd25de968a87a3192025-08-20T02:41:54ZengMDPI AGAgronomy2073-43952025-03-0115370910.3390/agronomy15030709Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear RegressionXuelin Wang0Qinqin Huang1Dong Wu2Jinhua Xie3Ming Cao4Jun Liu5Hefei Meteorological Bureau, Hefei 230041, ChinaHefei Meteorological Bureau, Hefei 230041, ChinaCollege of Resources and Environment, Anhui Agricultural University, Hefei 230036, ChinaHefei Meteorological Bureau, Hefei 230041, ChinaHefei Meteorological Bureau, Hefei 230041, ChinaHefei Meteorological Bureau, Hefei 230041, ChinaScientific management and environmental regulation of facility strawberries depends on the level of accurate prediction and forecasting of low temperature freezes in plastic greenhouses during winter and spring strawberry cultivation. Accurate identification of potential factors affecting layer-by-layer minimum temperatures in plastic greenhouses and selection of optimal forecasting methods are important for safe strawberry production. However, the identification of important drivers of minimum temperatures in plastic greenhouses and the prediction of potential drivers of use are still unclear. In this study, we used Classification and Regression Tree (CART) to identify the importance of the potential factors affecting the minimum temperatures at different depths and different heights of plastic greenhouses. Random forest (RF), back-propagation (BP), and multiple linear regression (MLR) were used to establish the minimum temperature prediction models for plastic greenhouses at different depths and heights, respectively. The results showed that T<sub>smin10</sub>, T<sub>smin25</sub>, T<sub>amin150</sub>, T<sub>amin320</sub>, and T<sub>amin150</sub> were the most important variables explaining the changes in minimum temperatures at heights T<sub>smin25</sub>, T<sub>smin10</sub>, T<sub>smin2</sub>, T<sub>amin150</sub>, and T<sub>amin320</sub> respectively. RF, BP performed much better than MLR, as it showed much lower error indices (AE and RMSE) and higher R<sup>2</sup> than MLR. The superiority of RF and BP in predicting minimum temperatures is related to their ability to deal with non-linear and hierarchical relationships between minimum temperatures and predictors. The low-temperature frost protection and fine management of strawberries in the Changfeng area can be related to the prediction method of minimum temperature in plastic greenhouses constructed in this study.https://www.mdpi.com/2073-4395/15/3/709<i>Fragaria</i> × <i>ananassa</i> Duchlow-temperature stressstrawberry growing seasonrandom forestback propagationmicroclimate regulation and control |
| spellingShingle | Xuelin Wang Qinqin Huang Dong Wu Jinhua Xie Ming Cao Jun Liu Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear Regression Agronomy <i>Fragaria</i> × <i>ananassa</i> Duch low-temperature stress strawberry growing season random forest back propagation microclimate regulation and control |
| title | Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear Regression |
| title_full | Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear Regression |
| title_fullStr | Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear Regression |
| title_full_unstemmed | Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear Regression |
| title_short | Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear Regression |
| title_sort | predicting minimum temperatures of plastic greenhouse during strawberry growing in changfeng china a comparison of machine learning algorithms and multiple linear regression |
| topic | <i>Fragaria</i> × <i>ananassa</i> Duch low-temperature stress strawberry growing season random forest back propagation microclimate regulation and control |
| url | https://www.mdpi.com/2073-4395/15/3/709 |
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