标题： 模拟大豆豆象种群温湿度耦合动力学及评估PCM-NN算法的预测性能 显示英文标题

标题： Modeling the Temperature-Humidity Coupling Dynamics of Soybean Pod Borer Population and Assessing the Predictive Performance of the PCM-NN Algorithm

摘要： 在全球气候变化和农业全球化的背景下，大豆生产日益受到害虫爆发的威胁，其中豆荚螟（学名：Leguminivora glycinivorella，通常称为大豆荚蜂）是一种主要的害虫种类。这种害虫分布广泛，尤其是在中国东北地区，这是中国主要的大豆产区，其爆发对产量和质量都产生了显著影响。尽管统计模型和机制模型已被用于害虫预测，但现有方法往往难以有效整合气候因素与害虫动态，并且表达能力不足。为解决这些局限性，本研究提出了一种基于物理信息神经网络（PINNs）的新颖害虫预测方法。具体而言，我们构建了一个逻辑型常微分方程（ODE），该方程结合了微气候因素、温度、湿度和时间，以描述大豆荚蜂种群的时间动态。该ODE模型被嵌入到PINN框架中，开发出害虫相关性模型神经网络（PCM-NN），用于联合推断由微气候驱动的参数函数alpha(T, H, t）并拟合害虫种群动态。我们使用2020年至2023年7月至9月期间在吉林省长春市收集的大豆荚蜂每日监测数据对PCM-NN进行了评估。实验结果表明，PCM-NN在保持生物可解释性的同时表现出强大的非线性表示能力，为在多因素气候条件下进行害虫建模和预测提供了一条可行的路径。这种方法为农业害虫监测、预防和控制策略提供了有价值的支持。 显示英文摘要

摘要： Against the backdrop of global climate change and agricultural globalization, soybean production is increasingly threatened by pest outbreaks, with Leguminivora glycinivorella (commonly known as the soybean pod borer) being a major pest species. This pest is widely distributed, particularly in northeastern China, the country's primary soybean-producing region, where its outbreaks have significantly affected both yield and quality. Although statistical and mechanistic models have been applied to pest forecasting, existing approaches often fail to effectively integrate climatic factors with pest dynamics and lack sufficient expressive power. To address these limitations, this study proposes a novel pest prediction method based on Physics-Informed Neural Networks (PINNs). Specifically, we formulate a logistic-type ordinary differential equation (ODE) that incorporates microclimate factors, temperature, humidity, and time, to describe the temporal dynamics of the soybean pod borer population. This ODE model is embedded into the PINN framework to develop the Pest Correlation Model Neural Network (PCM-NN), which is used to jointly infer the microclimate-driven parameter function alpha(T, H, t) and fit the pest population dynamics. We evaluate PCM-NN using daily monitoring data of soybean pod borer collected in Changchun, Jilin Province, from July to September during 2020-2023. Experimental results demonstrate that PCM-NN preserves biological interpretability while exhibiting strong nonlinear representational capacity, offering a feasible pathway for pest modeling and forecasting under multi-factor climatic conditions. This approach provides valuable support for agricultural pest monitoring, prevention, and control strategies.