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Evaluating RGB Imaging and Multispectral Active and Hyperspectral Passive Sensing for Assessing Early Plant Vigor in Winter Wheat

机译:评估RGB成像以及多光谱主动和高光谱被动传感技术用于评估冬小麦的早期植物活力

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摘要

Plant vigor is an important trait of field crops at early growth stages, influencing weed suppression, nutrient and water use efficiency and plant growth. High-throughput techniques for its evaluation are required and are promising for nutrient management in early growth stages and for detecting promising breeding material in plant phenotyping. However, spectral sensing for assessing early plant vigor in crops is limited by the strong soil background reflection. Digital imaging may provide a low-cost, easy-to-use alternative. Therefore, image segmentation for retrieving canopy cover was applied in a trial with three cultivars of winter wheat (Triticum aestivum L.) grown under two nitrogen regimes and in three sowing densities during four early plant growth stages (Zadok’s stages 14–32) in 2017. Imaging-based canopy cover was tested in correlation analysis for estimating dry weight, nitrogen uptake and nitrogen content. An active Greenseeker sensor and various established and newly developed vegetation indices and spectral unmixing from a passive hyperspectral spectrometer were used as alternative approaches and additionally tested for retrieving canopy cover. Before tillering (until Zadok’s stage 20), correlation coefficients for dry weight and nitrogen uptake with canopy cover strongly exceeded all other methods and remained on higher levels (R² > 0.60***) than from the Greenseeker measurements until tillering. From early tillering on, red edge based indices such as the NDRE and a newly extracted normalized difference index (736 nm; ~794 nm) were identified as best spectral methods for both traits whereas the Greenseeker and spectral unmixing correlated best with canopy cover. RGB-segmentation could be used as simple low-cost approach for very early growth stages until early tillering whereas the application of multispectral sensors should consider red edge bands for subsequent stages.
机译:植物活力是田间作物生长早期的重要特征,它会影响杂草的抑制,养分和水分的利用效率以及植物的生长。需要高通量的技术对其进行评估,并有望在早期生长阶段进行营养管理并在植物表型鉴定中检测出有前途的育种材料。然而,用于评估作物中早期植物活力的光谱感测受到强大的土壤背景反射的限制。数字成像可以提供低成本,易于使用的替代方案。因此,在2017年的一项试验中,在三个植物播种的四个早期阶段(Zadok的第14-32阶段),在两个氮素条件下以三种播种密度种植的三个冬小麦(Triticum aestivum L.)品种中,采用了图像分割技术来获取冠层覆盖。在相关分析中测试了基于成像的树冠覆盖层,以估计干重,氮吸收量和氮含量。主动式Greenseeker传感器以及各种已建立和新开发的植被指数以及从被动式高光谱仪获得的光谱分解被用作替代方法,并进行了额外的测试,以获取树冠覆盖率。分till之前(直到Zadok的第20阶段),与冠see之前的测高值相比,冠层覆盖的干重和氮吸收的相关系数大大超过了所有其他方法,并保持较高的水平(R²> 0.60 ***)。从早期分till开始,基于红边的指数(如NDRE)和新提取的归一化差异指数(736 nm;〜794 nm)被确定为两种性状的最佳光谱方法,而Greenseeker和光谱解混与冠层覆盖最相关。 RGB分割可用于非常早期的生长阶段直至分till早期的简单低成本方法,而多光谱传感器的应用应考虑后续阶段的红色边缘带。

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