基于田间环境及气象数据的甘蔗产量预测方法

Sugarcane Yield Prediction Method Based on Field Environmental and Meteorological Data

对2008-2017年间广西某甘蔗地装设的田间物联网获取的田间环境及气象数据进行了分析,研究通过这些数据预测甘蔗产量的可行性。对田间的环境数据(土壤含水率和土壤温度)和气象数据(降水量和空气温度)进行预处理,分别运用BP神经网络及遗传算法优化初始阈值的BP神经网络(GA-BP)对所选地块的甘蔗产量进行预测。结果表明,GA-BP神经网络模型的预测精度明显高于BP神经网络模型,R2达到0.9894,平均相对误差仅为0.64%。说明用GA-BP神经网络预测甘蔗产量是有效、可行的。

The agricultural internet of things has been gradually popularized in agricultural planting management,but most of the application scenarios are mainly focusing on guiding growers to fertilize and irrigate crops from its acquired field data.Environmental and meteorological data of the crop's entire growth season can also reflect the microscopic and macroscopic environment of crop growth,and hence influencing the final yield.Therefore,the environmental data (soil moisture content and soil temperature) and meteorological data (precipitation and air temperature) of a sugarcane field collected from a field IoTs system were used to build up yield predict models.The collected data was ranged from the year of 2008 to 2017,and the original BP neural network and an improved BP neural network based on genetic algorithm (GA-BP) were adopted to predict the yield.Comparing the prediction results of these two models,it was found that the prediction accuracy of the GA-BP neural network model was significantly higher than that of the BP neural network model,with its R 2 reaching 0.989 4,and its average relative prediction error was only 0.64%,which was also obviously lower than that of BP neural network model (5.66%).The result indicated that the GA-BP neural network was effective and feasible in predicting sugarcane production.