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Analysis of root images from auger sampling with a fast procedure: a case of application to sugar beet

机译：快速程序分析螺旋钻采样的根图像：在甜菜中的应用案例

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Manual line-intersect methods for estimating root length are being progressively replaced by faster and more accurate image analysis procedures. These methods even allow the estimation of some more root parameters (e.g., diameter), but still require preliminary labour-intensive operations. Through a task-specific macro function written in a general-purpose image analysis programme (KS 300 - Zeiss), the processing time of root images was greatly reduced with respect to skeletonisation methods by using a high-precision algorithm (Fibrelength). This has been previously proposed by other authors, and estimates length as a function of perimeter and area of the digital image of roots. One-bit binary images were acquired, aiming at large savings in computer memory, and automatic discrimination of roots against extraneous objects based on their elongation index (perimeter(2)/area), was performed successfully. Of four tested spatial resolutions (2.9, 5.9, 8.8, 11.8 pixel mm(-1)), in clean samples good accuracy in root length estimation was achieved at 11.8 pixel mm(-1), up to a root density of 5 cm cm(-2) on the scanner bed. This resolution is theoretically suitable for representing roots at least 85 mum wide. When dealing with uncleaned samples, a thick layer of water was useful in speeding up spreading of roots on the scanner bed and avoiding underestimation of their length due to overlaps with organic debris. A set of fibrous root samples of sugar beet (Beta vulgaris var. saccharifera L.) collected at harvest over two years at Legnaro (NE Italy) was analysed by applying the above procedure. Fertilisation with 100 kg ha(-1) of nitrogen led to higher RLD (root length density in soil) in shallow layers with respect to unfertilised controls, whereas thicker roots were found deeper than 80 cm of soil without nitrogen.
机译：用于估计根长度的手动线相交方法已逐渐被更快，更准确的图像分析程序所取代。这些方法甚至允许估计更多的根部参数（例如直径），但是仍然需要初步的劳动密集型操作。通过用通用图像分析程序（KS 300-Zeiss）编写的特定于任务的宏功能，相对于骨架化方法，使用高精度算法（光纤长度）可以大大减少根图像的处理时间。这是其他作者先前提出的，并且根据根的数字图像的周长和面积来估计长度。捕获了一位二进制图像，目的是节省大量的计算机内存，并成功地根据根的伸长指数（perimeter（2）/面积）自动识别了根与异物的区别。在四个测试的空间分辨率（2.9、5.9、8.8、11.8像素mm（-1））中，在干净的样本中，在11.8像素mm（-1）时，根长估计达到了良好的精度，最高根密度为5 cm cm （-2）在扫描仪床上。该分辨率在理论上适合于表示至少85毫米宽的根。当处理未清洁的样品时，厚的水层可用于加快根在扫描仪床上的铺开，并避免因与有机碎片重叠而低估其长度。通过应用上述程序，分析了在莱格纳罗（意大利东北）两年来收获时收集的一套甜菜纤维根样品（Beta vulgaris var。saccharifera L.）。与未施肥的对照相比，在100 kg ha（-1）的氮下施肥会导致浅层较高的RLD（土壤中的根长密度），而较厚的根则发现在80厘米以下的土壤中不含氮。

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