基于产量监测系统的小麦产量图生成与空间变异性分析  

Wheat Yield Distribution Map Generation and Spatial Variability Analysis Based on Yield Monitoring System

作　　者：刘仁杰 孙意凡 张振乾 张漫[1] 杨玮[2] 李民赞[1] LIU Renjie;SUN Yifan;ZHANG Zhenqian;ZHANG Man;YANG Wei;LI Minzan(Key Laboratory of Modern Precision Agriculture System Integration Research,Ministry of Education,China Agricultural University,Beijing 100083,China;Key Laboratory of Agricultural Information Acquisition Technology,Ministry of Agriculture and Rural Affairs,China Agricultural University,Beijing 100083,China)

机构地区：[1]中国农业大学现代精细农业系统集成研究教育部重点实验室,北京100083 [2]中国农业大学农业农村部农业信息获取技术重点实验室,北京100083

出　　处：《农业机械学报》2019年第B07期136-143,共8页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家重点研发计划项目(2016YFD0700300-2016YFD0700304);中国农业大学研究生实践教学基地建设项目(ZYXW037)

摘　　要：为了准确获取冬小麦农田产量空间差异性信息,提升产量监测系统的采集精度与产量空间分布图的插值精度,采用研发的收获机产量实时监测系统,从绘制准确的产量空间分布图入手,对2013-2015年的小麦产量数据进行了插值及空间变异性分析,结果表明:阈值滤波的预处理方法可以有效剔除产量异常值,还原真实田间产量分布情况。通过RMSE对比得出,普通克里金(OK)方法绘制的试验地块产量空间分布图插值精度更高,最小值为826.70kg/hm2,出现在2013年OK法指数模型中,搜索策略为椭圆形、最大相邻要素5个、最小相邻要素3个、1个扇区。由半方差函数拟合曲线参数得出3年产量空间变异性信息及监测系统的最优采样间距,分别为2013年与2014年的产量空间变异完全来源于空间自相关,2013年主要表现在2~12m的中尺度范围,2014年表现在2~5m的中尺度范围;2015年由随机因素引起的空间变异为25%,表现在2m以下的小尺度范围,空间自相关引起的变异为75%,表现在2~15m的中尺度范围;产量监测系统的采样间距应保持在2~10m,过小或过大将受到较大随机因素或插值精度降低的影响。Obtaining yield distribution map of farmland and analyzing the spatial difference of plot yield are important foundations for implementing precision farming.In order to accurately collect the spatial difference of yield,and at the same time,the acquisition accuracy of yield monitor system and the interpolation accuracy of the output spatial distribution map are improved.The self-developed real-time monitoring system of harvester was used.Based on accurate yield spatial distribution map,spatial variability analysis was conducted on wheat yield data from 2013 to 2015.Firstly,the results showed that the pretreatment method of threshold filtering can effectively eliminate outliers and restore the real yield distribution.Secondly,by comparing the RMSE values,it was determined that spatial distribution map of experimental plot yields drawn by the ordinary Kriging (OK) had higher interpolation accuracy.The minimum value of 826.70 kg/hm 2 appeared in the index mode of OK method for 2013,search strategy was elliptical,the largest adjacent element was 5,the smallest adjacent element was 3 and 1 sector.Finally,the curve parameters of semi-variance function were used to obtain the spatial variability information of three seasons and optimal sampling interval of the system.The spatial variation of yield in 2013 and 2014 were entirely caused by spatial autocorrelation.And that of 2013 was mainly in the mesoscale range of 2~12 m,and that of 2014 was in the mesoscale range of 2~5 m.The spatial variation caused by random factors in the 2015 was 25%,which was in the small scale range below 2 m.The spatial autocorrelation caused variation of 75%,which was in the mesoscale range of 2~15 m.The sampling interval of the system should be kept at 2~10 m.Too small or too large pitch was affected by large random factors or reduced interpolation accuracy.These results can be used to develop fine management decisions for farmland.

关 键 词：小麦 产量分布图 空间变异性分析 阈值滤波 普通克里金 IDW插值 

分 类 号：S24[农业科学—农业电气化与自动化]