机采棉田精准管理分区提取的研究
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摘要
棉田精准管理分区的建立能有效地降低棉田生产作业成本,减少环境污染,从而实现棉田的可持续利用。本研究以实现精准农业技术体系过程中的棉田精准管理分区为目标,引进美国AgLeader公司的Insight棉花产量监测系统,获取了2年的棉花产量实时数据,围绕此产量数据进行了误差的分析与处理,针对处理后的棉花产量数据进行了时间和空间变异性分析,并由此运用插值方法生成了高精度的棉花产量空间分布图,最终应用k均值聚类算法建立了棉田精准管理分区图。本文研究主要包括以下五个方面的内容：
(1)为加快棉花产量监测系统的国产化进程,实时检测研发的产量监测系统的工作状况,也为进一步的优化监测系统提供有效的、可靠的数据支持,研究设计了室内棉花在线测产试验台。针对引进的Insight棉花产量监测系统,在室内在线棉花测产试验台上进行试验检验,检验表明,其在我国新疆棉区能较好的工作,为后期的Insight棉花产量监测系统田间试验研究提供了可靠性依据。
(2)将引进的美国AgLeader公司的Insight棉花产量监测系统安装在美国John Deer 9970采棉机上,进行了持续的田间试验实践,进一步了解了棉花产量监测系统的组成、工作过程及各组件的工作原理、设置与标定方法,并成功获取了2年的棉花产量实时数据。试验结果表明,在正确安装棉花产量监测系统,正确设置所需参数,正确对采摘头高度、行驶距离、棉田面积和棉花重量进行标定的前提下,系统得到的产量数据才更真实、可靠。
(3)通过结合田间测产试验与棉花收获经验设计的一种数据过滤器,剔除掉了获取的产量数据中的所包含的系统误差,粗大误差和随机误差；对比了误差处理前后建立的棉花产量空间分布图,发现经过误差处理后的产量空间分布图中的特异点明显减少,分布图平滑性和聚类性也有了明显提高。
(4)对经过误差分析与处理后的两年的棉花产量数据进行了时间和空间上的变异性分析,分析表明,两年的棉花产量数据在时间变异性上表现不是很强,而在空间变异上属于中等强度变异,满足精准农业管理分区划分和实施变量作业管理的要求。继而利用ArcGIS软件中地统计分析功能,采用普通克里格插值,选取了拟合的最优模型,插值建立了高精度的产量空间分布图。
(5)通过对两年的棉花产量实时数据进行归一化处理和插值计算,得到了两年综合的棉花产量空间分布图,应用k均值聚类算法建立了棉田精准管理分区图,并对建立的管理分区图采用不同尺度的窗口平滑过滤掉了斑点或碎片,最终确定的最适宜管理分区数为4,最适宜的过滤空间尺度为18m。
The establishment of accurate management zones cotton can effectively reduce the operating costs of cotton field and environmental pollution, as a result, achieving the sustainable use of cotton field. This study is to realize the accurate management zones of cotton field, which is one of the process of implementation of precision agriculture technology system, so import the Insight cotton yield monitor system of the AgLeader Company which is come from the U.S. Then, by using this equipment I got 2 years real-time data of cotton yield, and then analyzed and processed the errors of this data, also analyzed temporal and spatial variability in view of the processed data, and through the use of highly accurate interpolation method to generate spatial distribution map of cotton yield, ultimately, applied the k means clustering algorithm for establishing accurate management zoning map of cotton field. This study includes the following four aspects:
(1) In order to accelerate the localization process of cotton yield monitoring system, detect the output of R & D working conditions of it in real-tine, but also to provide effective, reliable data to support for further optimization of monitoring system, researched and designed an indoor and online test bench of cotton yield.Put the the import of Insight for the cotton yield monitoring system in the interior line of cotton yield monitor test platform to examine, and the testing showed that it can be better to work in the Xinjiang cotton area of China, and provides a latter part of field test by using the the Insight cotton yield monitoring system with Reliability basis.
(2) Installed the Insight cotton yield monitor system of the AgLeader Company, which is come from the U.S, on the John Deer 9970 cotton picker to do sustainable field experiments. According to this, on the one hand, further understood the composition of the cotton yield monitoring system, working process and the components working principle, setup and calibration methods; on the other hand, successfully obtained 2 years real-time data on cotton production. The results showed that cotton yield in the monitoring system must be properly installed and correctly set the required parameters; the correct height of the picking head, driving distance, the weight of cotton and cotton area under the premise of the calibration, only in this way, the yield data can be more realistic and reliable.
(3)Through combination of field experiments and cotton harvest, a data filter was designed to weed out the errors from the cotton yield data, which contained the system errors, gross errors and random errors; comparing the establishment of cotton yield spatial distribution map around the processing errors, found that, after treatment, the singular points in cotton yield spatial distribution map were significantly reduced, the smoothness and cluster distribution were also markedly improved.
(4)Analyzed temporal and spatial variability in view of the processed two years of cotton yield data, and it showed that two years of cotton yield data on the variation in temporal performance was not very strong, and in the spatial variation was on a medium intensity variation, which met the requirements of precision agriculture management zones and operations management variables. After that, used statistical analysis function of the ArcGIS software, adopted ordinary Kriging to select the best fitting model for the yield of high precision interpolation spatial distribution map.
(5) In order to establish a two-year comprehensive spatial distribution map of cotton yield, Adopted normalized processing and interpolation to the 2 years real-time data of cotton yield,then,k means clustering algorithm was applied to establish the precise management zoning map of cotton field, and also establish management zoning map window by using different scales of smoothing filter to weed out spots or debris. Ultimately, determined that the most appropriate management of the number of partitions was 4, the most appropriate spatial scale of the filter was 18m.
(1)为加快棉花产量监测系统的国产化进程,实时检测研发的产量监测系统的工作状况,也为进一步的优化监测系统提供有效的、可靠的数据支持,研究设计了室内棉花在线测产试验台。针对引进的Insight棉花产量监测系统,在室内在线棉花测产试验台上进行试验检验,检验表明,其在我国新疆棉区能较好的工作,为后期的Insight棉花产量监测系统田间试验研究提供了可靠性依据。
(2)将引进的美国AgLeader公司的Insight棉花产量监测系统安装在美国John Deer 9970采棉机上,进行了持续的田间试验实践,进一步了解了棉花产量监测系统的组成、工作过程及各组件的工作原理、设置与标定方法,并成功获取了2年的棉花产量实时数据。试验结果表明,在正确安装棉花产量监测系统,正确设置所需参数,正确对采摘头高度、行驶距离、棉田面积和棉花重量进行标定的前提下,系统得到的产量数据才更真实、可靠。
(3)通过结合田间测产试验与棉花收获经验设计的一种数据过滤器,剔除掉了获取的产量数据中的所包含的系统误差,粗大误差和随机误差；对比了误差处理前后建立的棉花产量空间分布图,发现经过误差处理后的产量空间分布图中的特异点明显减少,分布图平滑性和聚类性也有了明显提高。
(4)对经过误差分析与处理后的两年的棉花产量数据进行了时间和空间上的变异性分析,分析表明,两年的棉花产量数据在时间变异性上表现不是很强,而在空间变异上属于中等强度变异,满足精准农业管理分区划分和实施变量作业管理的要求。继而利用ArcGIS软件中地统计分析功能,采用普通克里格插值,选取了拟合的最优模型,插值建立了高精度的产量空间分布图。
(5)通过对两年的棉花产量实时数据进行归一化处理和插值计算,得到了两年综合的棉花产量空间分布图,应用k均值聚类算法建立了棉田精准管理分区图,并对建立的管理分区图采用不同尺度的窗口平滑过滤掉了斑点或碎片,最终确定的最适宜管理分区数为4,最适宜的过滤空间尺度为18m。
The establishment of accurate management zones cotton can effectively reduce the operating costs of cotton field and environmental pollution, as a result, achieving the sustainable use of cotton field. This study is to realize the accurate management zones of cotton field, which is one of the process of implementation of precision agriculture technology system, so import the Insight cotton yield monitor system of the AgLeader Company which is come from the U.S. Then, by using this equipment I got 2 years real-time data of cotton yield, and then analyzed and processed the errors of this data, also analyzed temporal and spatial variability in view of the processed data, and through the use of highly accurate interpolation method to generate spatial distribution map of cotton yield, ultimately, applied the k means clustering algorithm for establishing accurate management zoning map of cotton field. This study includes the following four aspects:
(1) In order to accelerate the localization process of cotton yield monitoring system, detect the output of R & D working conditions of it in real-tine, but also to provide effective, reliable data to support for further optimization of monitoring system, researched and designed an indoor and online test bench of cotton yield.Put the the import of Insight for the cotton yield monitoring system in the interior line of cotton yield monitor test platform to examine, and the testing showed that it can be better to work in the Xinjiang cotton area of China, and provides a latter part of field test by using the the Insight cotton yield monitoring system with Reliability basis.
(2) Installed the Insight cotton yield monitor system of the AgLeader Company, which is come from the U.S, on the John Deer 9970 cotton picker to do sustainable field experiments. According to this, on the one hand, further understood the composition of the cotton yield monitoring system, working process and the components working principle, setup and calibration methods; on the other hand, successfully obtained 2 years real-time data on cotton production. The results showed that cotton yield in the monitoring system must be properly installed and correctly set the required parameters; the correct height of the picking head, driving distance, the weight of cotton and cotton area under the premise of the calibration, only in this way, the yield data can be more realistic and reliable.
(3)Through combination of field experiments and cotton harvest, a data filter was designed to weed out the errors from the cotton yield data, which contained the system errors, gross errors and random errors; comparing the establishment of cotton yield spatial distribution map around the processing errors, found that, after treatment, the singular points in cotton yield spatial distribution map were significantly reduced, the smoothness and cluster distribution were also markedly improved.
(4)Analyzed temporal and spatial variability in view of the processed two years of cotton yield data, and it showed that two years of cotton yield data on the variation in temporal performance was not very strong, and in the spatial variation was on a medium intensity variation, which met the requirements of precision agriculture management zones and operations management variables. After that, used statistical analysis function of the ArcGIS software, adopted ordinary Kriging to select the best fitting model for the yield of high precision interpolation spatial distribution map.
(5) In order to establish a two-year comprehensive spatial distribution map of cotton yield, Adopted normalized processing and interpolation to the 2 years real-time data of cotton yield,then,k means clustering algorithm was applied to establish the precise management zoning map of cotton field, and also establish management zoning map window by using different scales of smoothing filter to weed out spots or debris. Ultimately, determined that the most appropriate management of the number of partitions was 4, the most appropriate spatial scale of the filter was 18m.
引文
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