Summer School on Image Analysis for Plant Phenotyping

Summer school

Summer School on Image Analysis for Plant Phenotyping

Are you looking for a complete overview of image analysis techniques for automatic plant phenotyping? The course will provide a mixture of underlying theory and practical hands-on training. During the course you will have the opportunity to work on real plant image analysis problems so you can apply it directly in practice.

Organised by Wageningen Academy, Wageningen Plant Research, Wageningen Food & Biobased Research

Mon 2 July 2018 until Fri 6 July 2018

Duration 5 days
Setup Campus WUR
Venue Wageningen

Our Approach

In this programme, a mixture of lectures from experts from Wageningen University & Research and leading international experts in this domain, in the field, are combined with hands-on training. During the course there is ample time to discuss the issues of importance to your company / institutions with the experts. We will pay attention to the following subjects:
• Images and image quality
• Noise and noise removal
• Segmentation
• Multi- and hyperspectral imaging
• Classical machine learning
• 3D imaging
• Open brainstorms & case studies

    Target Group

    This Summer School is primarily intended for researchers working on automatic phenotyping and technical experts in breeding companies who use, or plan to use, image analysis. If you have doubts whether you belong to the target group or have enough relevant basic knowledge, please don’t hesitate to contact us!


    After following this Summer School you know about the latest insights in imaging techniques applied to plant phenotyping. Furthermore, you will be able to apply them in daily practice in your experiments.

    Course Leaders

    The Summer School is given by dr. Gerrit Polder and Rick van de Zedde MSc, Wageningen University & Research.

    Artist Impressions of Plant Phenotyping Tools (WUR)

    I found the Summer School on Image Analysis for Plant Phenotyping provided both a good foundation for biologists interested in image analysis as well as a comprehensive overview for computer scientists of the challenges faced when dealing with plant data. I feel the methods were most applicable to grad students, postdocs, and other researchers analyzing a small number of images (~10-100 images).
    Matt Colgan (Blue River Technology), participant 2016 edition

    I don’t know what really surprised me, but like the kind of openness of the staff and the people here, their friendliness, and openness. It is not necessarily surprising, but coming from a place like New York, were everybody is dead straight on their own work, it is refreshing to see such a collaborative environment.
    Justin Zabilansky (Aerofarms), participant 2017 edition