Data from: A new approach for rapid measurement of directional root responses to neighbors using the root centroid

Measuring directional root placement is critical for understanding plant
responses to their belowground environment, and particularly their
competing neighbors. Directional root placement is commonly measured using
image analysis of roots growing in transparent pots (rhizoboxes), where
the length of the root system of the target plants is tracked. However,
tracking roots with a soil background can be highly challenging,
particularly in competition studies, where two or more root systems are
intertwined within the same experimental setup. In this study, we propose
a new approach for measuring directional root placement in competitive
setups, with two methods that calculate the centroid of the root system
without measuring overall root length. In the first method, the centroid
is calculated by marking all the intersection points of the target plant
roots along a fixed number of equally spaced horizontal lines superimposed
on the image. In the second method, the centroid is calculated from a
contour line (polygon) created by marking only the peripheral intersection
points. We developed an open-access, interactive Python algorithm that
automates and standardizes the centroid calculation for both methods. We
validated these methods by comparing them to the centroid calculated from
the traditional root length measurements using results from two rhizobox
competition experiments, with either uniform or patchy soil nutrient
distribution. While the two methods offer a more rapid and standardized
calculation of the root system centroid, they differ in their investment
time vs. accuracy levels, particularly when root density is heterogeneous.
By focusing solely on a few sample points from the root system or its
contour line rather than tracking the entire root system, this approach
offers a potentially faster way for measuring directional root placement.
The centroid approach could therefore facilitate the study of plant
responses to belowground competition, enabling more efficient tracking
over time and across multiple samples.