Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.
Electrical and Computer Engineering Department, North Carolina State University, Raleigh, 27695, NC, USA.
New Phytol. 2024 Dec;244(5):2101-2108. doi: 10.1111/nph.20151. Epub 2024 Oct 6.
Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with RhizoVision Explorer, a free and open-source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.
根系在农业和自然系统中对于确定植物生产力和土壤碳输入非常重要。有时，样本中的根系数量太多，无法放入单个扫描图像中，因此将样本分为几个扫描，并且没有标准的方法来汇总数据。在这里，我们描述并验证了两种用于跨多个扫描标准化测量的方法：图像拼接和统计聚合。我们开发了一个 Python 脚本，该脚本可以识别属于同一样本的图像，并返回一个更大的拼接图像。使用 RhizoVision Explorer（一个免费的开源软件）对这些拼接图像和原始图像进行处理。开发了一个 R 脚本，该脚本可以识别属于同一样本的数据行，并应用正确的统计方法，为每个样本返回一个单独的数据行。使用来自北方泥炭地和北极的柳枝稷、杨树以及各种树木和石南科灌木物种的示例图像比较了这两种方法。两种方法的大多数根测量值几乎相同，除了中位数直径，统计聚合无法准确计算。我们相信这些方法的可用性将对根系生物学界有用。
