Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning
Leaf wetness duration (LWD) is a key driving variable for peat and disease control in greenhouse management, and depends upon irrigation, rainfall, and dewfall. However, LWD measurement is often replaced by its estimation from other meteorological variables, with associated uncertainty due to the mo...
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2020
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| Online Access: | http://hdl.handle.net/10835/7543 |
| _version_ | 1789406520297914368 |
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| author | Wang, Hui Sánchez Molina, Jorge Antonio Li, Ming Rodríguez Díaz, Francisco |
| author_facet | Wang, Hui Sánchez Molina, Jorge Antonio Li, Ming Rodríguez Díaz, Francisco |
| author_sort | Wang, Hui |
| collection | DSpace |
| description | Leaf wetness duration (LWD) is a key driving variable for peat and disease control in greenhouse management, and depends upon irrigation, rainfall, and dewfall. However, LWD measurement is often replaced by its estimation from other meteorological variables, with associated uncertainty due to the modelling approach used and its calibration. This study uses the decision learning tree method (DLT) for calibrating four LWD models—RH threshold model (RHM), the dew parameterization model (DPM), the classification and regression tree model (CART) and the neural network model (NNM)—whose performances in reproducing measured data are assessed using a large dataset. The relative importance of input variables in contributing to LWD estimation is also computed for the models tested. The LWD models were evaluated at two different greenhouse locations: in a Chinese (CN) greenhouse over three planting seasons (April 2014–October 2015) and in a Spanish (ES) greenhouse over four planting seasons (April 2016–February 2018). Based on multi-evaluation indicators, the models were given a ranking for their assessment capabilities during calibration (in the Spanish greenhouse from April 2016 to December 2016 and in the Chinese greenhouse from April 2014 to November 2014). The models were then evaluated on an independent set of data, and the obtained areas under the receiver operating characteristic curve (AUC) of the LWD models were over 0.73. Therein, the best LWD model in this case was the NNM, with positive predict values (PPVs) of 0.82 (SP) and 0.90 (CN), and mean absolute errors (MAEs) of 1.85 h (SP) and 1.30 h (CN). Consequently, the DLT can decrease LWD estimation error by calibrating the model threshold and choosing black box model input variables. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-7543 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-75432023-04-12T19:25:53Z Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning Wang, Hui Sánchez Molina, Jorge Antonio Li, Ming Rodríguez Díaz, Francisco leaf wetness threshold data classification data mining technology dew temperature Leaf wetness duration (LWD) is a key driving variable for peat and disease control in greenhouse management, and depends upon irrigation, rainfall, and dewfall. However, LWD measurement is often replaced by its estimation from other meteorological variables, with associated uncertainty due to the modelling approach used and its calibration. This study uses the decision learning tree method (DLT) for calibrating four LWD models—RH threshold model (RHM), the dew parameterization model (DPM), the classification and regression tree model (CART) and the neural network model (NNM)—whose performances in reproducing measured data are assessed using a large dataset. The relative importance of input variables in contributing to LWD estimation is also computed for the models tested. The LWD models were evaluated at two different greenhouse locations: in a Chinese (CN) greenhouse over three planting seasons (April 2014–October 2015) and in a Spanish (ES) greenhouse over four planting seasons (April 2016–February 2018). Based on multi-evaluation indicators, the models were given a ranking for their assessment capabilities during calibration (in the Spanish greenhouse from April 2016 to December 2016 and in the Chinese greenhouse from April 2014 to November 2014). The models were then evaluated on an independent set of data, and the obtained areas under the receiver operating characteristic curve (AUC) of the LWD models were over 0.73. Therein, the best LWD model in this case was the NNM, with positive predict values (PPVs) of 0.82 (SP) and 0.90 (CN), and mean absolute errors (MAEs) of 1.85 h (SP) and 1.30 h (CN). Consequently, the DLT can decrease LWD estimation error by calibrating the model threshold and choosing black box model input variables. 2020-01-17T10:24:34Z 2020-01-17T10:24:34Z 2019-01-17 info:eu-repo/semantics/article 2073-4441 http://hdl.handle.net/10835/7543 en https://www.mdpi.com/2073-4441/11/1/158 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | leaf wetness threshold data classification data mining technology dew temperature Wang, Hui Sánchez Molina, Jorge Antonio Li, Ming Rodríguez Díaz, Francisco Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning |
| title | Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning |
| title_full | Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning |
| title_fullStr | Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning |
| title_full_unstemmed | Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning |
| title_short | Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning |
| title_sort | improving the performance of vegetable leaf wetness duration models in greenhouses using decision tree learning |
| topic | leaf wetness threshold data classification data mining technology dew temperature |
| url | http://hdl.handle.net/10835/7543 |
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