The significance of image compression in plant phenotyping applications
Massimo Minervini A D , Hanno Scharr B and Sotirios A. Tsaftaris A C
+ Author Affiliations
- Author Affiliations
A Pattern Recognition and Image Analysis, IMT Institute for Advanced Studies, Lucca, Piazza S. Francesco, 19, 55100 Lucca, Italy.
B Institute of Bio- and Geosciences: Plant Sciences, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany.
C Present address: Institute of Digital Communications, School of Engineering, University of Edinburgh, Alexander Graham Bell, King’s Buildings, Edinburgh EH9 3JL, UK.
D Corresponding author. Email: m.minervini@imtlucca.it
Functional Plant Biology 42(10) 971-988 https://doi.org/10.1071/FP15033
Submitted: 11 February 2015 Accepted: 1 July 2015 Published: 7 September 2015
Abstract
We are currently witnessing an increasingly higher throughput in image-based plant phenotyping experiments. The majority of imaging data are collected using complex automated procedures and are then post-processed to extract phenotyping-related information. In this article, we show that the image compression used in such procedures may compromise phenotyping results and this needs to be taken into account. We use three illuminating proof-of-concept experiments that demonstrate that compression (especially in the most common lossy JPEG form) affects measurements of plant traits and the errors introduced can be high. We also systematically explore how compression affects measurement fidelity, quantified as effects on image quality, as well as errors in extracted plant visual traits. To do so, we evaluate a variety of image-based phenotyping scenarios, including size and colour of shoots, leaf and root growth. To show that even visual impressions can be used to assess compression effects, we use root system images as examples. Overall, we find that compression has a considerable effect on several types of analyses (albeit visual or quantitative) and that proper care is necessary to ensure that this choice does not affect biological findings. In order to avoid or at least minimise introduced measurement errors, for each scenario, we derive recommendations and provide guidelines on how to identify suitable compression options in practice. We also find that certain compression choices can offer beneficial returns in terms of reducing the amount of data storage without compromising phenotyping results. This may enable even higher throughput experiments in the future.
Additional keywords: computer vision, growth analysis, imaging sensor, lossless, lossy, optical flow.
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