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Using aerial imagery and digital photography to monitor growth and yield in winter wheat

机译:使用空中图像和数码摄影来监测冬小麦的增长和产量

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摘要

Monitoring wheat (Triticum aestivum L.) performance throughout the growing season provides information on productivity and yield potential. Remote sensing tools have provided easy and quick measurements without destructive sampling. The objective of this study was to evaluate genetic variability in growth and performance of 20 wheat genotypes under two water regimes (rainfed and irrigated), using spectral vegetation indices (SVI) estimated from aerial imagery and percentage ground cover (%GC) estimated from digital photos. Field experiments were conducted at Bushland, Texas in two growing seasons (2014-2015 and 2015-2016). Digital photographs were taken using a digital camera in each plot, while a manned aircraft collected images of the entire field using a 12-band multiple camera array Tetracam system at three growth stages (tillering, jointing and heading). Results showed that a significant variation exists in SVI, %GC, aboveground biomass and yield among the wheat genotypes mostly at tillering and jointing. Significant relationships for %GC from digital photo at jointing was recorded with Normalized Difference Vegetation Index (NDVI) at tillering (coefficient of determination, R-2 = 0.84, p 0.0001) and with %GC estimated from Perpendicular Vegetation Index (PVI) at tillering (R-2 = 0.83, p 0.0001). Among the indices, Ratio Vegetation Index (RVI), Green-Red VI, Green Leaf Index (GLI), Generalized DVI (squared), DVI, Enhanced VI, Enhanced NDVI, and NDVI explained 37-99% of the variability in aboveground biomass and yield. Results indicate that these indices could be used as an indirect selection tool for screening a large number of early-generation and advanced wheat lines.
机译:在整个生长季节监测小麦(Triticum Aestivum L.)表现提供了有关生产力和产量潜力的信息。遥感工具提供了轻松快速的测量而无需破坏性取样。本研究的目的是评估在两次水域(雨量和灌溉)下的20个小麦基因型的生长和性能的遗传变异,使用从空中图像估计的谱植被指数(SVI),估计从数字估计的地面覆盖(%GC)估计相片。在两个生长季节的德克萨斯州(2014-2015和2015-2016)中在德克萨斯州进行了现场实验。数码照片在每个绘图中使用数码相机进行,而在三个生长阶段(分蘖,伸直和标题),使用12频带多个摄像机阵列四屠杀系统收集整个场的图像。结果表明,SVI,GC%,地上生物量和小麦基因型的产量大部分存在显着变化,主要是在分蘖和接头处。从连接的数码照片的大量GC的显着关系被突出的差异差异植被指数(NDVI)(测定系数,R-2 = 0.84,P <0.0001),并从垂直植被指数(PVI)估计%GC分蘖(R-2 = 0.83,P <0.0001)。在指数中,比率植被指数(RVI),绿红VI,绿叶指数(GLI),广义DVI(Squared),DVI,增强VI,增强的NDVI和NDVI在地上生物质的变异性中解释了37-99%和产量。结果表明,这些指标可用作筛选大量早期和高级小麦线的间接选择工具。

著录项

  • 来源
    《International journal of remote sensing》 |2019年第18期|6905-6929|共25页
  • 作者单位

    Texas A&M Univ Dept Soil & Crop Sci College Stn TX 77843 USA|Texas A&M AgriLife Res Amarillo TX 79119 USA;

    Texas A&M Univ Dept Soil & Crop Sci College Stn TX 77843 USA;

    Texas A&M Univ Dept Soil & Crop Sci College Stn TX 77843 USA;

    Texas A&M AgriLife Res Amarillo TX 79119 USA;

    Texas A&M AgriLife Res Amarillo TX 79119 USA;

    USDA ARS Stoneville MS 38776 USA;

    Texas A&M AgriLife Res Amarillo TX 79119 USA;

    Texas A&M AgriLife Res Amarillo TX 79119 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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