Divide and conquer: using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods.

Abstract

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.

Code and data availability

The paper's Data availability statement deposits the root scan imageset, the Python image-concatenation script, and the R statistical-aggregation/figure code on Zenodo with explicit DOIs, making the paper-specific phenotyping images and analysis code publicly actionable. Since the Zenodo deposit URLs are not among the,