通过使用红外热像仪技术获得冬小麦冠层不同温度值, 计算得到冬小麦主要需水阶段水分胁迫指标ICWSI(infrared crop water stress index)。 并根据此数据, 使用一次灌溉周期中3个时段不同的ICWSI的平均值作为输入因子, 相应实测冬小麦产量作为输出因子, 建立了BP神经网络模型对冬小麦的产量进行预测, 本文采用三层BP神经网络, 其拓扑结构为3-5-1, 数据归一化处理后收敛性能增强。 预测结果显示, 平均相对误差最大只有3.42%; 为了证实这一方法的优越性, 同时建立了基于ICWSI和冬小麦产量关系的非线性函数的预测模型, 预测结果与实际产量值进行比较, 平均相对误差最大达到了18.87%。 两种预测方法得到的不同预测结果表明, 将红外热像仪技术与BP神经网络预测方法相结合, 可以成功用来预测冬小麦产量, 比使用非线性函数预测的效果更好, 精度更高, 可靠性更强, 可以用于实际生产需要。

Using a thermal camera to obtain canopy temperatures for winter wheat, an infrared crop water stress index (ICWSI) was calculated in the main water-requirement stage. The performance of a BP neural network was tested with ICWSI values for three different periods in one irrigation circle as independent input factors and observed winter wheat yield after harvest as the output. The topology of the neural network was 3-5-1, and after data normalization, convergence performance was enhanced. Results showed that the maximum relative error was only 3.42%. To confirm the superiority of this method, a common nonlinear regression model was also built to compare the predictions with ICWSI values and the observed yield of winter wheat, but the maximum relative error of this model was higher (18.87%). Comparison between these two mathematical methods shows that the approach of combining thermal camera technology with a BP neural network prediction model, which is more precise for nonlinear prediction, was sufficiently better than other models to predict the winter wheat yield successfully and accurate enough to meet production requirements.