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Videometer微根窗多光谱自动表型成像

最近,来自哥本哈根大学、丹麦理工大学以及丹麦Videometer公司的专家在Plant and Soil上发表了题为A multispectral camera system for automated minirhizotron image analysis的文章,这篇文章相较上一次文章就平台构建的描述有了更深入的研究,参见Frontiers in Plant Sciences,Screening of Barley Resistance Against Powdery Mildew  by Simultaneous High-Throughput Enzyme Activity Signature Profiling and Multispectral Imaging。研究人员利用了Videometer公司开发的多光谱表型研究平台,波段范围从400-1000nm。值得一提的是,歌本哈根大学4个表型平台中的3个是利用了Videometer开发的表型设备。详细文章请大家查询该文章。Videometer公司是欧洲知名的工程设计公司,在机器视觉领域的研究具有世界水准,其所开发的多光谱成像系统是目前世界上应用案例最多、应用范围最广、发表文章最多的系统。

北京博普特科技有限公司是丹麦Videometer公司中国区总代理,全面负责其系列产品在中国市场的推广、销售和售后服务。

根系是植物的重要器官,但对根性状评估,特别自然野外条件下对深层土壤根系的测量是比较困难的。在田间或半田间通用的根系生长调查技术是微根管法。即便如此,后续的手工定量分析过程耗时且易出错。

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科研人员开发了微根管多光谱成像系统以及后续的图像分析策略用于根系自动检测。研究选择了从可见光(VIS)到近红外(NIR)的五个光谱波长,基于反射不同,利用多变量像素分组来增强活根成像显示; 背景噪音通过根管增加滤波加以抑制。在2个时间点,利用网格交叉方法对The system was tested against manual analysis of grid intersections for both 春大麦 (Hordeum vulgare L.)以及多年生黑麦草 (Lolium perenne L.)栽培种进行了手工分析验证。活根图像在湿润底土条件下进行拍摄,上次作物死根还有残留。

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结果

本研究土壤条件下, NIR 反射 (940 nm)区分根际组分的能力,与紫外以及蓝光波段 (405nmand450nm)相比,非常有限。光谱数据多变量图像分析结合根管增强以及阈值方法可实现活根的自动检测。自动图像分析显著复制了手工网格交叉方法处理的相同的图像的根密度情况。尽管出现了一些因具有与活根相同反射率模式的水滴延长结构以及白垩石结构导致的分类错误,在每个根管中,系统提供了相同甚至更好的总根长度的基因型差异检测。

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结论

在维根管研究中,多光谱成像系统可实现活根自动检测。系统研究所需时间显著低于手工网格交叉法。根系分区的灵活训练策略为应用到其它根际组分以及其它感兴趣土壤类型的研究提供了可能。

Aims

Roots are vital organs for plants, but the assessment of root traits is difficult, particularly in deep soil layers under natural field conditions. A popular technique to investigate root growth under field or semi-field conditions is the use of minirhizotrons. However, the subsequent manual quantification process is time-consuming and prone to error.

Methods

We developed a multispectral minirhizotron imaging system and a subsequent image analysis strategy for automated root detection. Five wavelengths in the visible (VIS) and near-infrared (NIR) spectrum are used to enhance living roots by a multivariate grouping of pixels based on differences in reflectance; background noise is suppressed by a vesselness enhancement filter. The system was tested against manual analysis of grid intersections for both spring barley (Hordeum vulgare L.) and perennial ryegrass (Lolium perenne L.) cultivars at two time-points. The images of living roots were captured in wet subsoil conditions with dead roots present from a previous crop.

Results

Under the soil conditions used in the study, NIR reflectance (940 nm), provided limited ability to separate between rhizosphere components, compared to reflectance in the violet and blue light spectrum (405 nm and 450 nm). Multivariate image analysis of the spectral data, combined with vesselness enhancement and thresholding allowed for automated detection of living roots. Automated image analysis largely replicated the root intensity found during manual grid intersect analysis of the same images. Although some misclassification occurred, caused by elongated structures of dew and chalkstone with similar reflectance pattern as living root, the system provided similar or in some cases improved detection of genotypic differences in the total root length within each tube.

Conclusion

The multispectral imaging system allows for automated detection of living roots in minirhizotron studies. The system requires considerably less time than traditional manual recording using grid intersections. The flexible training strategy used for root segmentation offers hope for the transfer to other rhizosphere components and other soil types of interest.

Keywords

Automated Imaging Minirhizotron Multispectral Root Soil 

文章全文:

Svane S F, Dam E B, Carstensen J M, Thorup-Kristensen K. A multispectral camera system for automated minirhizotron image analysis. Plant Soil. https://doi.org/10.1007/s11104-019-04132-8

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