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评价 RGB 成像技术和多光谱主动及高光谱被动感测在评估冬小麦早期活力中的应用。

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

机构信息

Chair of Plant Nutrition, Technical University of Munich, 85354 Freising, Germany.

出版信息

Sensors (Basel). 2018 Sep 3;18(9):2931. doi: 10.3390/s18092931.

Abstract

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 ( 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.

摘要

植物活力是田间作物早期生长阶段的一个重要特征,影响杂草抑制、养分和水分利用效率以及植物生长。因此,需要评估其的高通量技术,这对于早期生长阶段的养分管理和植物表型中潜在优良材料的检测具有广阔的应用前景。然而,利用光谱传感来评估作物早期的植物活力受到强烈的土壤背景反射的限制。数字成像技术可能提供一种低成本、易于使用的替代方法。因此,本研究在 2017 年的一个试验中,应用图像分割技术从三个冬小麦品种( L.)的冠层中提取覆盖度,该试验在两个氮素水平和三个播种密度下进行,共涉及四个早期生长阶段(Zadok 阶段 14-32)。基于成像的冠层覆盖度与干重、氮吸收量和氮含量进行相关分析。使用主动 GreenSeeker 传感器以及各种已建立和新开发的植被指数和被动高光谱仪的光谱解混,作为替代方法进行了测试,以评估其对冠层覆盖度的反演能力。在分蘖前(直到 Zadok 阶段 20),干重和氮吸收量与冠层覆盖度的相关系数明显优于其他所有方法,并且与 GreenSeeker 测量值相比,一直保持着更高的水平(R²>0.60***)。从早期分蘖开始,基于红边的指数(如 NDRE 和新提取的归一化差异指数(736nm;~794nm))被确定为这两个性状的最佳光谱方法,而 GreenSeeker 和光谱解混与冠层覆盖度的相关性最好。在早期分蘖之前,RGB 分割可作为一种简单的低成本方法用于非常早期的生长阶段,而在随后的阶段,应考虑使用多光谱传感器的红边波段。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0df6/6163924/b0f8a547acde/sensors-18-02931-g001.jpg

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