That will not come as a surprise for the root researchers reading this post: analysing plant root system architectures and measuring root functional traits are challenging tasks! The first challenge is methodological: How to efficiently acquire root images? And what is the ‘best’ method to analyse them? The answers to these questions are not easy to find and will depend on a number of factors, such as the traits or variables you would like to measure and the level of automation you wish to have, for instance. Guillaume has already written several nice posts on these topics here.
A couple of month ago, I was discussing with senior colleagues about the conditions to (have a chance to) be tenured in Belgium. One of them told that the first thing the evaluation committee will look at will be my h-index. He also told me that, below a h-index of 10, I have few chances to even be considered. He admitted it to be bad, but that it the case anyway…
If you have ever analysed root system images, you might now the unspoken truth: it requires a great deal of time and efforts! Either you work with an automated tracing software (such as EZ-Rhizo or RootSystemAnalyzer) and as soon as the root systems are getting old (more than 10 days) you’ll need to spend quite some time correcting the detection; or you work with a semi-automated tool (such as SmartRoot or RootNav) and you’ll have to go through every single image to trace the roots. The good thing is that you know you’ll get great data out of it! Architectural data are extremely rich and can be analysed in multiple ways. However, the downside of the story is that you will use only a fraction of the data and the rest might be lost. This is what the Root System Markup Language is about.
Nobody questions it anymore: image analysis is an essential tool in plant sciences. From the object detection algorithm used by the microscope software to the dedicated ImageJ plugin for rosette area measurements, images analysis tools and methods are presents in many plant researchers’ pipeline. In this post, I will try to give a short overview of the existing plant image analysis tools (mainly with data coming from the plant-image-analysis.org website). I will also discuss some of the current challenges that the field is facing and I will give you my two cents on how we might resolve them in the future.
About two years ago, we (Laura Mathieu, Pierre Tocquin, Claire Périlleux and myself) started to think about an efficient way to monitor the development of the Arabidopsis root system from the seedling to the flowering stages.
As I scientist, I write a lot and in various ways. I write notes and ideas. I write projects. Papers. Reviews. Blog posts. Codes. Pretty much everything. As a consequence, I need the right tools to write, organise and find all these document. I also like to keep my folders well organised (by projects / experiments / …). This is important, as I want my different project folders to be complete and contains everything related to the project.
Published papers are only the tip of the research iceberg. The visible part is often a fraction of the results acquired over the years while the hidden part is made of sweat and perseverance. Of repeated cycles of failures and success. Of (lonely) hours in the lab or in front of a computer. Of writing, submitting, re-writing, re-submitting. The hidden part is where the people (we, as scientists) are. So, often, when I read a paper I wonder about the story behind the project. When did it start and how long did it take to achieve it all? Why did they choose such approach? In short, what is the story behind this paper?
Not all papers on root image analysis present a new software and therefore I do not present them on this website. However, some of them are worth reading since they can bring new perspectives or methods that can be useful for all root researchers. So in order to be able to share all the interesting papers I read on root image analysis, I decide to create a group on the Mendeley website (Mendeley is a free citation manager with several sharing and social features). I am therefore delighted to invite you to consult or even join this group and hope it will turn out to be useful for everyone interested.
One way to take root picture using a camera is just to hold your root system by the collar and take a picture. While this technique might be the fastest and the easiest one, it is also to easiest way to get a bad picture.
Using a scanner is the best way to have picture with a high contrast and resolution. However, the speed of the acquisition is rather slow compared to a camera, specially if the resolution is high.
If you want to take a picture or scan a root system, one of the requirements is that this root system is clean of soil. While this is not an issue if the plant is grown in aeroponic or hydroponics, it is important for field- / pot-grown plants.The easiest way to clean roots is to simply let them soak in water, eventually with soap for several minutes. Then take them out and use pressurised water to remove the mud.If you need to clean smaller root or finer particles, you can use a brush to gently remove them from the roots.
One way to take root picture using a camera is to put the root system on a flat surface and take picture from above. This technique is fast and can be used with large root system.
The image acquisition step is a critical one as it will determine the quantity of information contained in the final image. Fortunately, good images can be obtain fairly simply without buying fancy expensive material.
In this post, I will try to point out the most important characteristics you will have to consider while choosing your root image analysis software.
The quality of an image is central if you want to get useful information out of it. Even the best software will not be able to analyse crappy images, so it is crucial to think about what makes a good image and how to acquire it.
Many root image analysis procedures have a thresholding step in their workflow (e.i. WinRHIZO or EZ-RHIZO). The thresholding step aims at segmenting the original image (fig. 1A) in two parts: the “object” (the roots) and the “background”, and to assign to each of them a different pixel value (typically 1 and 0). The subsequent binary image (fig. 1B) can be used, for instance, to generate a “root skeleton” (fig. 1C) or to estimate root diameter.