基于neuralnet人工神经网络的农作物预测模型研究

Research on crop prediction model based on neuralnet artificial neural network

选用改进后精度更稳定、可视化的neuralnet包人工神经网络模型对粮食产量进行预测。结果显示,在验证样本中粮食产量预测值与实测值的相关系数为0.9910398,平均相对误差为0.006339629,10次交叉检验方法得到的均值误差平均值为0.001229604;表明预测值与实测值误差较小,拟合效果近乎直线,其中相对误差比spss多层感知器、nnet包分别提高了3.9%、1.1%。该结果表明neuralnet神经网络预测模型具有较好的预测性和可行性,可作为新技术权衡农作物的利用分配,促使农产品经济的不断增长和推进农业产业的可持续发展。

The improved neural network model with more stable precision and visualization was used to predict the grain yield.The results showed that the correlation coefficient between the predicted value and the measured value of grain yield was 0.9910398,the relative error was 0.006339629,and the average error of 10 cross tests was 0.001229604.The results showed that the relative error between the predicted value and the measured value was small,and the fitting effect was close to a straight line.The relative error was 3.9%and 1.1%higher than that of spss multi-layer perceptron and nnet packet respectively.The results showed that the neural network prediction model had good prediction and feasibility.It can be used as a new technology to balance the utilization and distribution of crops,and promote the continuous growth of agricultural products economy and the sustainable development of the agricultural industry.