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Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning
Jiazheng Zhu1,2, Xize Huang1,2, Xiaoyu Liang1,2
1Sanya Institute of Breeding and Multiplication, Hainan University, Sanya 572025, China.
Abstract:
Powdery mildew, caused by Erysiphe quercicola, 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 R2 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.
Insights
Accurate prediction of rubber tree powdery mildew is crucial for disease management. Machine learning models, particularly Kernel Ridge Regression, effectively forecast disease progression based on spore concentration and environmental factors.
Area of Science:
- Plant Pathology
- Agricultural Meteorology
- Machine Learning in Agriculture
Background:
- Powdery mildew (caused by *Erysiphe quercicola*) significantly reduces natural rubber production in China.
- This airborne pathogen spreads rapidly, posing epidemic risks under favorable conditions.
- Predicting and managing rubber tree powdery mildew is a critical challenge for the industry.
Purpose of the Study:
- To investigate the impact of spore concentration, environmental factors (temperature, humidity), and infection time on rubber tree powdery mildew progression.
- To develop and evaluate machine learning models for predicting the disease index of rubber tree powdery mildew.
- To establish a technical foundation for improved forecasting of airborne forest diseases.
Main Methods:
- Utilized six distinct machine learning model construction methods.
- Employed spore concentration, temperature, humidity, and infection time as predictive variables.
- Used the disease index of powdery mildew in rubber trees as the response variable.
Main Results:
- Spore concentration directly correlates with disease progression and severity; higher concentrations lead to faster, more severe development.
- Optimal relative humidity for powdery mildew development is 80% RH, with temperature influencing this relationship.
- All models simulated disease progression accurately, with Kernel Ridge Regression (KRR) showing the highest accuracy (R² train: 0.978, R² test: 0.964).
Conclusions:
- Machine learning models provide a robust technical foundation for predicting rubber tree powdery mildew.
- The KRR model offers high accuracy, reducing labor intensity compared to traditional prediction methods.
- This research offers valuable insights for forecasting airborne forest diseases, improving disease management strategies.
