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首页> 外文期刊>Acta Horticulturae >Data assimilation to improve states estimation of a dynamic greenhouse tomatoes crop growth model
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Data assimilation to improve states estimation of a dynamic greenhouse tomatoes crop growth model

机译：数据同化改进动态温室番茄作物生长模型的估计

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摘要

Mechanistic dynamic crop growth models have parameters values depending on the variety and culture method. For these reasons, the models must be calibrated under the specific conditions where they will be used. It is important to incorporate an adaptation technique for these parameters. The reduced simulation model TOMGRO was developed to predict the potential growing of greenhouse tomatoes. This model considers as state variables: the number of nodes, leaf area index and total dry matter. In this work, a data assimilation method based on the Unscented Kalman Filter (UKF) was developed, in order to improve the prediction on the three state variables of the TOMGRO model, by incorporating measurements from the crop. The equations of the model were solved numerically by using the fourth order Runge-Kutta integration method in the MATLAB-Simulink environment. The Kalman Filter was used to estimate the model states with different error levels. The filter performance was evaluated by the root mean squared error (RMSE) and also the Mean Absolute Error (MAE). The simulation results showed a better fit of the TOMGRO model when states are estimated using an UKF than when the model is calibrated by a standard procedure. Based on the statistic test results, it is concluded that the UKF successfully improves the prediction of the three state variables of the TOMGRO model. Therefore, the Unscented Kalman Filter is an efficient data assimilation method for non-linear dynamics crop growth models under greenhouses.
机译：机械动态作物生长模型根据品种和培养方法具有参数值。由于这些原因，必须在将使用它们的特定条件下校准模型。结合这些参数的适配技术很重要。制定了减少的仿真模型Tomgro，以预测温室西红柿的潜在生长。该模型认为是状态变量：节点，叶面积指数和总干物质的数量。在这项工作中，开发了一种基于UNSCENTED KALMAN滤波器（UKF）的数据同化方法，以改善来自作物的测量来改善TOMGRO模型的三种状态变量的预测。通过使用Matlab-Simulink环境中的第四阶runge-Kutta集成方法，使用第四阶runge-Kutta集成方法来解决模型的等式。卡尔曼滤波器用于估计具有不同误差级别的模型状态。通过根均方误差（RMSE）和平均绝对误差（MAE）评估过滤器性能。当使用UKF估计状态时，仿真结果显示了TOMGRO模型的拟合，而不是通过标准过程校准模型时的状态。基于统计测试结果，得出结论，UKF成功提高了Tomgro模型的三种状态变量的预测。因此，Unscented Kalman滤波器是在温室下的非线性动力学作物生长模型的有效数据同化方法。

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