Course summary and further reading

Well done for making it to the end of the course!

Hopefully you have learned something about image analysis in plant phenotyping which will be useful to you. We also hope we have sparked further interest in the topic!

During the course you have learned about image processing and image analysis for plant phenotyping. We have introduced a popular image processing tool (Fiji) as well as some coding approaches to image analysis. You have seen how to write some basic code in Python to access and work with images, in both Fiji and using other libraries like OpenCV.

We have tried to show some of the power of image analysis, and also that you need to think carefully about the kind of processing you are doing, that assumptions are met, and that algorithms are doing what you think they are.

As well as this, we have tried to introduce some specific image analysis topics which will show you the kinds of tasks image analysis can accomplish. We have tried to pick topics which are relevant to plant phenotyping in particular, covering a variety of data and image types. Of course, image analysis is a huge area and there is only so much we can introduce here, so below we present some sources of further reading and investigation.

Plant phenotyping tools and datasets

Lists software tools and dataset relevant to particular phenotyping tasks. https://www.quantitative-plant.org

Nottingham Annotated Crop Image Dataset https://plantimages.nottingham.ac.uk

Fiji

A Fiji Scripting tutorial in Python https://syn.mrc-lmb.cam.ac.uk/acardona/fiji-tutorial

Coding

OpenCV Python tutorials https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html

Networking

The UK plant phenotyping network, which runs events, workshops and funding schemes in the area of plant phenotyping. https://www.phenomuk.net

Additional courses

There will also be later courses from the DataCampp collection on coding and data, with an emphasis on plant phenotyping:

• Coding for Robotics and Data Capture (expected Summer 2022)
• Machine Learning for Image Data (expected Summer/Autumn 2022)
• Introduction to robotics for bioscientists (expected Summer 2022)