A Machine Learning Model for Photorespiration Response to Multi-Factors
Photorespiration results in a large amount of leaf photosynthesis consumption. However, there are few studies on the response of photorespiration to multi-factors. In this study, a machine learning model for the photorespiration rate of cucumber leaves’ response to multi-factors was established. It...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Horticulturae |
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| Online Access: | https://www.mdpi.com/2311-7524/7/8/207 |
| _version_ | 1797523735751688192 |
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| author | Kunpeng Zheng Yu Bo Yanda Bao Xiaolei Zhu Jian Wang Yu Wang |
| author_facet | Kunpeng Zheng Yu Bo Yanda Bao Xiaolei Zhu Jian Wang Yu Wang |
| author_sort | Kunpeng Zheng |
| collection | DOAJ |
| description | Photorespiration results in a large amount of leaf photosynthesis consumption. However, there are few studies on the response of photorespiration to multi-factors. In this study, a machine learning model for the photorespiration rate of cucumber leaves’ response to multi-factors was established. It provides a theoretical basis for studies related to photorespiration. Machine learning models of different methods were designed and compared. The photorespiration rate was expressed as the difference between the photosynthetic rate at 2% O<sub>2</sub> and 21% O<sub>2</sub> concentrations. The results show that the XGBoost models had the best fit performance with an explained variance score of 0.970 for both photosynthetic rate datasets measured using air and 2% O<sub>2</sub>, with mean absolute errors of 0.327 and 0.181, root mean square errors of 1.607 and 1.469, respectively, and coefficients of determination of 0.970 for both. In addition, this study indicates the importance of the features of temperature, humidity and the physiological status of the leaves for predicted results of photorespiration. The model established in this study performed well, with high accuracy and generalization ability. As a preferable exploration of the research on photorespiration rate simulation, it has theoretical significance and application prospects. |
| first_indexed | 2024-03-10T08:47:16Z |
| format | Article |
| id | doaj.art-e645ba42f03a4fb09a7acab1e716a619 |
| institution | Directory Open Access Journal |
| issn | 2311-7524 |
| language | English |
| last_indexed | 2024-03-10T08:47:16Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj.art-e645ba42f03a4fb09a7acab1e716a6192023-11-22T07:50:48ZengMDPI AGHorticulturae2311-75242021-07-017820710.3390/horticulturae7080207A Machine Learning Model for Photorespiration Response to Multi-FactorsKunpeng Zheng0Yu Bo1Yanda Bao2Xiaolei Zhu3Jian Wang4Yu Wang5Department of Protected Horticulture, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, ChinaDepartment of Protected Horticulture, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, ChinaDepartment of Protected Horticulture, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, ChinaDepartment of Protected Horticulture, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, ChinaDepartment of Protected Horticulture, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, ChinaDepartment of Protected Horticulture, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, ChinaPhotorespiration results in a large amount of leaf photosynthesis consumption. However, there are few studies on the response of photorespiration to multi-factors. In this study, a machine learning model for the photorespiration rate of cucumber leaves’ response to multi-factors was established. It provides a theoretical basis for studies related to photorespiration. Machine learning models of different methods were designed and compared. The photorespiration rate was expressed as the difference between the photosynthetic rate at 2% O<sub>2</sub> and 21% O<sub>2</sub> concentrations. The results show that the XGBoost models had the best fit performance with an explained variance score of 0.970 for both photosynthetic rate datasets measured using air and 2% O<sub>2</sub>, with mean absolute errors of 0.327 and 0.181, root mean square errors of 1.607 and 1.469, respectively, and coefficients of determination of 0.970 for both. In addition, this study indicates the importance of the features of temperature, humidity and the physiological status of the leaves for predicted results of photorespiration. The model established in this study performed well, with high accuracy and generalization ability. As a preferable exploration of the research on photorespiration rate simulation, it has theoretical significance and application prospects.https://www.mdpi.com/2311-7524/7/8/207photorespirationenvironmentmodelmachine learning |
| spellingShingle | Kunpeng Zheng Yu Bo Yanda Bao Xiaolei Zhu Jian Wang Yu Wang A Machine Learning Model for Photorespiration Response to Multi-Factors Horticulturae photorespiration environment model machine learning |
| title | A Machine Learning Model for Photorespiration Response to Multi-Factors |
| title_full | A Machine Learning Model for Photorespiration Response to Multi-Factors |
| title_fullStr | A Machine Learning Model for Photorespiration Response to Multi-Factors |
| title_full_unstemmed | A Machine Learning Model for Photorespiration Response to Multi-Factors |
| title_short | A Machine Learning Model for Photorespiration Response to Multi-Factors |
| title_sort | machine learning model for photorespiration response to multi factors |
| topic | photorespiration environment model machine learning |
| url | https://www.mdpi.com/2311-7524/7/8/207 |
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