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Remote sensing as a scouting tool for weed and crop anomalies.

机译:遥感作为杂草和农作物异常的侦查工具。

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摘要

Hand-held and aerial imagery were collected in two soybean fields in 2001 and 2002 to determine the utility of remotely sensed data for distinguishing sicklepod, pitted morningglory, entireleaf morningglory, horsenettle, and soybean. For hand-held data, discriminant models were created using multiple indices. From the pooled data set over years and locations, classification accuracies ranged from 29 to 99%. Nearly all wavelengths found for discriminating sicklepod, pitted morningglory, entireleaf morningglory, horsenettle, and soybean were located in the near-infrared portion of the electromagnetic spectrum. For aerial images, weed maps were constructed using total weed species at ground data sampling point. Using weed maps as reference, aerial image classification accuracies were 81 to 90% correct.; Weed population estimates were collected in two fields in 1997 to 1999 and 2001 to 2002 to determine if previous years' weed populations could predict future infestations. The strongest correlations occurred with 1997 data to predict 1998 and 2001 to predict 2002 sicklepod populations. In addition, greater than 84% agreement was found for these years on one field. Stronger correlations in sequential years is to be expected due to more inherent variability present between distant years than sequential years. Overall, morningglory spp. populations were weakly correlated between years. Using 1998 population data to predict 1999 horsenettle infestations at the same spatial location resulted in correlations up to 0.76.; Two soybean fields were monitored in 2001 and 2002 to determine the ability of multispectral imagery to be used for locating and classifying crop anomalies. Three image collection dates per location for each year was used in the supervised classification analysis. Crop anomalies included planter problems, soil problems, weed escapes, and stressed soybean plants in general. Accuracies for using remotely sensed data as a scouting tool ranged from 50 to 100%. As the number of anomalies observed from aerial imagery decreased, the number of anomalies found from directed scouting increased, thus providing higher accuracies in the latter part of the growing season.
机译:2001年和2002年在两个大豆田中收集了手持式和航拍图像,以确定遥感数据可用于区分镰刀形,有凹纹的牵牛花,全叶牵牛花,荨麻和大豆。对于手持数据,使用多个索引创建判别模型。从多年和不同地点的汇总数据集中,分类准确性范围为29%至99%。几乎所有用于区分镰刀形,凹纹牵牛花,全叶牵牛花,马蹄莲和大豆的波长都位于电磁波谱的近红外部分。对于航空图像,在地面数据采样点使用总杂草物种构建了杂草图。使用杂草图作为参考,航空影像分类的准确度为81%到90%。 1997年至1999年和2001年至2002年在两个领域中收集了杂草种群估计数,以确定前几年的杂草种群是否可以预测未来的侵扰。与1997年预测1998年的数据和2001年预测2002年的镰刀菌种群的数据之间的相关性最强。此外,这些年来在一个领域上达成了超过84%的协议。由于相较于相继年份,遥远年份之间存在更多的固有变异性,因此可以预期相继年份中的相关性会更强。总体而言,牵牛花属。人口之间的相关性较弱。使用1998年的人口数据预测在同一空间位置的1999年马蹄铁的侵袭,相关系数最高为0.76。 2001年和2002年对两个大豆田进行了监测,以确定将多光谱图像用于定位和分类作物异常的能力。在监督分类分析中,每年每个位置使用三个图像收集日期。作物异常通常包括播种机问题,土壤问题,杂草逃逸和大豆受压植物。使用遥感数据作为搜寻工具的准确性范围为50%至100%。随着从航空影像中观察到的异常数量减少,从定向侦察中发现的异常数量增加,因此在生长季节的后期提供了更高的准确性。

著录项

  • 作者

    Kelley, Franklin Shane.;

  • 作者单位

    Mississippi State University.;

  • 授予单位 Mississippi State University.;
  • 学科 Agriculture Agronomy.; Remote Sensing.
  • 学位 M.S.
  • 年度 2003
  • 页码 79 p.
  • 总页数 79
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 农学(农艺学);遥感技术;
  • 关键词

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