Beijing Key Laboratory of Environment Friendly Management On Fruit Diseases and Pests in North China, Institute of Plant Protection, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Institute of Plant Protection, Liaoning Academy of Agriculture Sciences, Shenyang, China.
Int J Biometeorol. 2023 Jun;67(6):993-1002. doi: 10.1007/s00484-022-02419-7. Epub 2023 May 30.
Reliable disease management can guarantee healthy plant production and relies on the knowledge of pathogen prevalence. Modeling the dynamic changes in spore concentration is available for realizing this purpose. We present a novel model based on a time-series modeling machine learning method, i.e., a long short-term memory (LSTM) network, to analyze oomycete Plasmopara viticola sporangia concentration dynamics using data from a 4-year field experiment trial in North China. Principal component analysis (PCA)-based high-quality input screening and simulation result calibration were performed to ensure model performance, obtaining a high determination coefficient (0.99), a low root mean square error (0.87), and a low mean bias error (0.55), high sensitivity (91.5%), and high specificity (96.5%). The impact of the variability of relative factors on daily P. viticola sporangia concentrations was analyzed, confirming that a low daily mean air temperature restricts pathogen development even during a long period of high humidity in the field.
可靠的疾病管理可以保证植物的健康生产，这依赖于对病原体流行情况的了解。通过模拟孢子浓度的动态变化，可以实现这一目标。我们提出了一种新的模型，该模型基于时间序列建模机器学习方法，即长短期记忆（LSTM）网络，利用来自华北地区为期 4 年的田间试验数据来分析卵菌纲霜霉属（Plasmopara viticola）游动孢子浓度的动态变化。通过基于主成分分析（PCA）的高质量输入筛选和模拟结果校准来确保模型性能，得到了高决定系数（0.99）、低均方根误差（0.87）和低平均偏差误差（0.55）、高灵敏度（91.5%）和高特异性（96.5%）。分析了相对因素的可变性对每日卵菌纲霜霉属游动孢子浓度的影响，证实即使在田间长时间高湿度的情况下，较低的日平均气温也会限制病原体的发展。
