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Related Experiment Video

Updated: Feb 14, 2026

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

Citizen crowds and experts: observer variability in image-based plant phenotyping.

M Valerio Giuffrida^1,2, Feng Chen¹, Hanno Scharr³

¹1School of Engineering, Institute of Digital Communications, The University of Edinburgh, Edinburgh, EH9 3FB UK.

|February 17, 2018

Summary

Observer variability in plant phenotyping is minimal and does not impact leaf count trends. Citizen scientists can accurately annotate leaf counts, offering a viable alternative to experts for image-based phenotyping tasks.

Keywords:

Agreement Citizen-science Crowdsourcing Image-based Observer Phenotyping Variability

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Last Updated: Feb 14, 2026

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Published on: December 12, 2015

Area of Science:

Plant Science
Computational Biology
Bioinformatics

Background:

Image-based plant phenotyping is crucial for understanding genotype-environment interactions.
Image analysis and machine learning are key tools in phenotyping, but human observer input is relied upon as a gold standard.
Variability among observers, both experienced and non-experienced, in phenotyping tasks needs to be quantified.

Purpose of the Study:

To measure intra- and inter-observer variability in leaf count annotation.
To compare the performance of experienced vs. non-experienced observers and citizen scientists.
To evaluate the impact of specialized annotation tools and citizen-powered platforms on data accuracy.

Main Methods:

Comparison of experienced and non-experienced observers annotating leaf counts in *Arabidopsis thaliana* images.
Utilizing controlled studies with specialized annotation tools and a citizen-powered web platform.
Assessing leaf count variability using low and high-resolution images and comparing with a machine-learning algorithm.

Main Results:

Specialized tools reduce intra-observer variability in leaf counting.
Observer variability does not significantly affect longitudinal leaf count trend assessments.
Citizen scientists can provide statistically accurate leaf counts, comparable to experts.

Conclusions:

Non-experienced observers and citizen scientists can be valuable for image-based phenotyping annotation tasks with proper design.
Automated algorithms performing within observer variability are suitable alternatives.
Citizen-powered platforms can engage the public in important scientific research.