基于BP神经网络的马铃薯气候产量预报模型

为了准确预测马铃薯气候产量达到趋利避害的目的，利用1980—2015 年山西省大同市马铃薯产量及同期国家基准观象台观测到的气候资料，选用传统的统计回归方法和BP神经网络方法分别建立马铃薯产量预报模型。结果表明：通过二次函数曲线和最小二乘法确定马铃薯敏感期的气候因子是气温、日照和降水，其中降水对马铃薯产量的影响最大。通过改进的气候产量算法可以更好地反映气候要素与作物单产之间的函数关系。在Matlab 平台上训练精度设为0.005、学习率0.01 的BP神经网络方法可以很好地逼近非线性函数。用大于1/3 样本进行预报检验表明，在预报精度和拟合精度上，BP神经网络模型都明显优于传统的回归模型，BP神经网络方法在马铃薯产量预报中有具有非常广泛的应用前景。

Potato Climate Yield Prediction Model Based on BP Neural Network

The paper aims to predict accurately the potato yield in order to draw on advantages and avoid disadvantages of climate. Based on the potato yield data in Datong of Shanxi in 1980-2015 and the climate data observed by national reference observatory in the same period, we established a prediction model of potato yield by using traditional statistical regression method and BP neural network method. The results showed that: firstly, using the method of the quadric curve and the least-squares, the climatic factors in potato sensitive stage were temperature, sunshine and precipitation, and precipitation had the greatest impact on potato yield; secondly, the improved climate yield algorithm could better reflect the function relation between climatic factors and crop yield; thirdly, the method of BP neural network, by the precision of training set to 0.005 and the learning rate set to 0.01 could approach the nonlinear function well on the Matlab platform; fourthly, more than 1/3 samples in the forecast verification showed that BP neural network model was better than traditional methods on prediction accuracy and fitting precision, and BP neural network had a very broad applicationprospect in potato yield forecast.