Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning
Powdery mildew, caused by <i>Erysiphe quercicola</i>, is one of the primary diseases responsible for the reduction in natural rubber production in China. This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into an epid...
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MDPI AG
2025-08-01
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| author | Jiazheng Zhu Xize Huang Xiaoyu Liang Meng Wang Yu Zhang |
| author_facet | Jiazheng Zhu Xize Huang Xiaoyu Liang Meng Wang Yu Zhang |
| author_sort | Jiazheng Zhu |
| collection | DOAJ |
| description | Powdery mildew, caused by <i>Erysiphe quercicola</i>, is one of the primary diseases responsible for the reduction in natural rubber production in China. This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into an epidemic under favorable environmental conditions. Accurate prediction and determination of the prevention and control period represent both a critical challenge and key focus area in managing rubber-tree powdery mildew. This study investigates the effects of spore concentration, environmental factors, and infection time on the progression of powdery mildew in rubber trees. By employing six distinct machine learning model construction methods, with the disease index of powdery mildew in rubber trees as the response variable and spore concentration, temperature, humidity, and infection time as predictive variables, a preliminary predictive model for the disease index of rubber-tree powdery mildew was developed. Results from indoor inoculation experiments indicate that spore concentration directly influences disease progression and severity. Higher spore concentrations lead to faster disease development and increased severity. The optimal relative humidity for powdery mildew development in rubber trees is 80% RH. At varying temperatures, the influence of humidity on the disease index differs across spore concentration, exhibiting distinct trends. Each model effectively simulates the progression of powdery mildew in rubber trees, with predicted values closely aligning with observed data. Among the models, the Kernel Ridge Regression (KRR) model demonstrates the highest accuracy, the R<sup>2</sup> values for the training set and test set were 0.978 and 0.964, respectively, while the RMSE values were 4.037 and 4.926, respectively. This research provides a robust technical foundation for reducing the labor intensity of traditional prediction methods and offers valuable insights for forecasting airborne forest diseases. |
| format | Article |
| id | doaj-art-283eb8b0cf834fe6a5cfb6e6924c9844 |
| institution | Kabale University |
| issn | 2223-7747 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj-art-283eb8b0cf834fe6a5cfb6e6924c98442025-08-20T03:36:22ZengMDPI AGPlants2223-77472025-08-011415240210.3390/plants14152402Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine LearningJiazheng Zhu0Xize Huang1Xiaoyu Liang2Meng Wang3Yu Zhang4Sanya Institute of Breeding and Multiplication, Hainan University, Sanya 572025, ChinaSanya Institute of Breeding and Multiplication, Hainan University, Sanya 572025, ChinaSanya Institute of Breeding and Multiplication, Hainan University, Sanya 572025, ChinaSanya Institute of Breeding and Multiplication, Hainan University, Sanya 572025, ChinaSanya Institute of Breeding and Multiplication, Hainan University, Sanya 572025, ChinaPowdery mildew, caused by <i>Erysiphe quercicola</i>, is one of the primary diseases responsible for the reduction in natural rubber production in China. This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into an epidemic under favorable environmental conditions. Accurate prediction and determination of the prevention and control period represent both a critical challenge and key focus area in managing rubber-tree powdery mildew. This study investigates the effects of spore concentration, environmental factors, and infection time on the progression of powdery mildew in rubber trees. By employing six distinct machine learning model construction methods, with the disease index of powdery mildew in rubber trees as the response variable and spore concentration, temperature, humidity, and infection time as predictive variables, a preliminary predictive model for the disease index of rubber-tree powdery mildew was developed. Results from indoor inoculation experiments indicate that spore concentration directly influences disease progression and severity. Higher spore concentrations lead to faster disease development and increased severity. The optimal relative humidity for powdery mildew development in rubber trees is 80% RH. At varying temperatures, the influence of humidity on the disease index differs across spore concentration, exhibiting distinct trends. Each model effectively simulates the progression of powdery mildew in rubber trees, with predicted values closely aligning with observed data. Among the models, the Kernel Ridge Regression (KRR) model demonstrates the highest accuracy, the R<sup>2</sup> values for the training set and test set were 0.978 and 0.964, respectively, while the RMSE values were 4.037 and 4.926, respectively. This research provides a robust technical foundation for reducing the labor intensity of traditional prediction methods and offers valuable insights for forecasting airborne forest diseases.https://www.mdpi.com/2223-7747/14/15/2402rubber treedisease index<i>Erysiphe quercicola</i>predict modelmachine learning |
| spellingShingle | Jiazheng Zhu Xize Huang Xiaoyu Liang Meng Wang Yu Zhang Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning Plants rubber tree disease index <i>Erysiphe quercicola</i> predict model machine learning |
| title | Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning |
| title_full | Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning |
| title_fullStr | Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning |
| title_full_unstemmed | Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning |
| title_short | Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning |
| title_sort | prediction model of powdery mildew disease index in rubber trees based on machine learning |
| topic | rubber tree disease index <i>Erysiphe quercicola</i> predict model machine learning |
| url | https://www.mdpi.com/2223-7747/14/15/2402 |
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