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

被引：0
|
作者：
Liu R. [1 ]
Sun Y. [2 ]
Zhang Z. [2 ]
Zhang M. [1 ]
Yang W. [2 ]
Li M. [1 ]
机构：
[1] Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing
[2] Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2019年 / 50卷
关键词：
IDW; Ordinary Kriging; Spatial variability analysis; Threshold filtering; Wheat; Yield distribution map;
D O I：
10.6041/j.issn.1000-1298.2019.S0.022
中图分类号：
学科分类号：
摘要：
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/hm2 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. © 2019, Chinese Society of Agricultural Machinery. All right reserved.
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页码：136 / 143
页数：7
相关论文
共 20 条
  • [1] Li M., The technique of yield monitor and key equipment, Agriculture Network Information, 4, pp. 34-38, (2004)
  • [2] Zhang M., Research on grain yield information collection, processing and system integration technology, (2003)
  • [3] Van Ittersum M.K., Cassmam K.G., Grassini P., Et al., Yielrd gap analysis with local to global relevance-a review, Field Crops Res, 143, pp. 4-17, (2013)
  • [4] Gerstmann H., Doktord, Glaberc, Et al., PHASE: a geostatistical model for the Kriging-based spatial predictionof crop phenology using public phenological and climatological observations, Computers and Electronics in Agriculture, 127, pp. 726-738, (2016)
  • [5] Brusa D.J., Boogaardb H., Ceccarellib T., Et al., Geostatistical disaggregation of polygon maps of average crop yields by area-to-point Kriging, European Journal of Agronomy, 97, pp. 48-59, (2018)
  • [6] Li X., Optimizations of yield monitoring system for grain combine harvester and development on remote systems, (2015)
  • [7] Yang W., Investigating the impacts of soil erosion on soil quality and corn yield in the typical black soil region, (2016)
  • [8] Liu R., Zhang Z., Zhang M., Et al., Performance analysis and modelling of impact-based sensor in yield monitor system, 6th IFAC Conference on Bio-Robotics, pp. 613-618, (2018)
  • [9] Wang B., Li M., Zhang C., Et al., Development of grain flow sensor for yield monitor system, Transactions of the Chinese Society for Agricultural Machinery, 40, pp. 52-56, (2009)
  • [10] Zhang Z., Liu R., Zhang M., Et al., Design of real-time monitoring platform for grain yield based on mobile terminal, Transactions of the Chinese Society for Agricultural Machinery, 48, pp. 35-39, (2017)
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