基于XGBoost的土壤含水量传感器温度补偿模型研究  

Research on Temperature Compensation Model for Soil Moisture Content Sensors Based XGBoost

作　　者：沈欣 吴勇[1] 孟范玉[2] 张赓[1] 于景鑫 史凯丽 SHEN Xin;WU Yong;MENG Fan-yu;ZHANG Geng;YU Jing-xin;SHI Kai-li(National Agricultural Technology Extension Service Center,Beijing 100125,China;Beijing Agricultural Technology Extension Station,Beijing 100129,China;Key Laboratory of Agricultural Information Software and Hardware Product Quality Testing,Beijing 100097,China;Beijing Paideweiye Technology Development Co.Ltd,Beijing 100097,China)

机构地区：[1]全国农业技术推广服务中心,北京100125 [2]北京市农业技术推广站,北京100129 [3]农业信息软硬件产品质量检测重点实验室,北京100097 [4]北京派得伟业科技发展有限公司,北京100097

出　　处：《节水灌溉》2021年第8期13-18,共6页Water Saving Irrigation

基　　金：国家重点研发计划项目(2016YFD0201304);北京市科技计划项目(Z181100002418003);现代农业产业技术体系建设项目“国家玉米产业技术体系”(CARS-02-87);北京市农林科学院开放课题(KFZN2020W002)。

摘　　要：土壤含水量传感器数值测定的准确性是其应用于精准灌溉实现农业节水的前提,然而土壤温度的变化对土壤含水量传感器数值采集的偏差具有显著影响。研究的目的在于分析不同土壤温度对土壤含水量传感器测定影响,进一步提出基于XGBoost(eXtreme Gradient Boosting)的土壤含水量传感器温度补偿模型,并验证和对比其预测精度。研究中分别配制土壤含水量为10%、15%、20%、25%、35%的12组梯度湿土土样基准,记录传感器在各土样中0~45℃温度变化过程的读数,并将数据集划分后用于模型训练和测试。结果表明:同一土样基准中土壤含水量传感器读数随着土壤温度的升高而增加,各土样基准类别传感器读数最大值与最小值的变幅为[3.6%,7.9%],平均读数变幅为6.25%;所提出的XGBoost土壤含水量温度校正模型能够实现对传感器土壤含水量温度影响的补偿,对测试集的均方误差(MSE)、平均绝对误差(MAE)、均方根误差(RMSE)和决定系数(R2)分别为0.013%、0.825%、1.165%和0.973。此外,与其他基于树和常用的机器学习模型对比结果显示研究提出的XGBoost温度校正模型具有最佳预测精度。The accuracy of soil water content sensor measurement values is a prerequisite for its application in precision irrigation to achieve water conservation in agriculture,but the variation of soil temperature has a significant impact on the deviation of soil water content sensor values.The purpose of this study is to analyze the effect of different soil temperatures on the measurement of soil water content sensors,and to further propose an XGBoost(eXtreme Gradient Boosting)based temperature compensation model for soil water content sensors and to verify and compare its prediction accuracy.In this study,12 sets of gradient wet soil sample benchmarks with soil moisture content of 10%,15%,20%,25%,and 35%were prepared,the sensor readings during the temperature change process from 0°C to 45°C in each soil sample were recorded,and the data sets were divided and used for model training and testing.The results showed that:the soil water content sensor readings in the same soil sample benchmark increased with the increase of soil temperature,and the variation of the maximum and minimum sensor readings in each soil sample benchmark category was within[3.6%,7.9%],with an average reading variation of 6.25%;the proposed XGBoost soil water content temperature correction model could compensate the influence of the sensor soil moisture temperature.The mean square error(MSE),mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2)of the test set were 0.013%,0.825%,1.165%,and 0.973,respectively;moreover,the comparison results with other tree-based and commonly used machine learning models showed that the XGBoost temperature correction model proposed in the study had the best prediction accuracy.

关 键 词：土壤含水率 土壤水分 XGBoost 温度补偿 机器学习 传感器 

分 类 号：S274.2[农业科学—农业水土工程]