Zhu Jiazheng, Huang Xize, Liang Xiaoyu, Wang Meng, Zhang Yu
Sanya Institute of Breeding and Multiplication, Hainan University, Sanya 572025, China.
School of Tropical Agriculture and Forestry, Hainan University, Danzhou 571737, China.
Plants (Basel). 2025 Aug 3;14(15):2402. doi: 10.3390/plants14152402.
Powdery mildew, caused by , 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 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.
白粉病由[未提及病原体名称]引起，是导致中国天然橡胶产量下降的主要病害之一。这种病害是典型的气传病原体，其特点是能够通过气流传播，并在有利的环境条件下迅速发展成流行病。准确预测和确定防治时期是橡胶树白粉病管理中的关键挑战和重点关注领域。本研究调查了孢子浓度、环境因素和侵染时间对橡胶树白粉病病情发展的影响。通过采用六种不同的机器学习模型构建方法，以橡胶树白粉病的病情指数作为响应变量，孢子浓度、温度、湿度和侵染时间作为预测变量，建立了橡胶树白粉病病情指数的初步预测模型。室内接种实验结果表明，孢子浓度直接影响病害的发展和严重程度。较高的孢子浓度导致病害发展更快、严重程度增加。橡胶树白粉病发展的最佳相对湿度为80%RH。在不同温度下，湿度对病情指数的影响因孢子浓度而异，呈现出不同的趋势。每个模型都有效地模拟了橡胶树白粉病的病情发展，预测值与观测数据紧密吻合。在这些模型中，核岭回归（KRR）模型表现出最高的准确性，训练集和测试集的R值分别为0.978和0.964，而均方根误差（RMSE）值分别为4.037和4.926。本研究为降低传统预测方法的劳动强度提供了坚实的技术基础，并为预测气传森林病害提供了有价值的见解。
