2015
DOI: 10.1071/fp15033
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The significance of image compression in plant phenotyping applications

Abstract: We currently witness an increasingly higher throughput in image-based plant phenotyping experiments. The majority of imaging data are collected based on complex automated procedures, and are then post-processed to extract phenotyping related information. In this article we show that image compression used in such procedures may compromise phenotyping results and needs to be taken into account. We motivate the paper with three illuminating proof of concept experiments which demonstrate that compression (especia… Show more

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Cited by 10 publications
(4 citation statements)
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“…Furthermore, by relying on the Cloud, the additional computational needs to analyze higher throughput data can be readily met because of its immediate resource scalability, and the implementation of asynchronous upload mechanisms that are used by our device to send data to the Cloud. When the available network bandwidth or storage capacity is limited (which could occur in laboratories in countries with poorer Internet infrastructure), we can potentially integrate image compression algorithms within the Phenotiki device (Minervini and Tsaftaris, ; Minervini et al ., ).…”
Section: Discussionmentioning
confidence: 97%
See 1 more Smart Citation
“…Furthermore, by relying on the Cloud, the additional computational needs to analyze higher throughput data can be readily met because of its immediate resource scalability, and the implementation of asynchronous upload mechanisms that are used by our device to send data to the Cloud. When the available network bandwidth or storage capacity is limited (which could occur in laboratories in countries with poorer Internet infrastructure), we can potentially integrate image compression algorithms within the Phenotiki device (Minervini and Tsaftaris, ; Minervini et al ., ).…”
Section: Discussionmentioning
confidence: 97%
“…To facilitate the configuration and monitoring of the device, we deployed a web‐based graphical user interface to operate it remotely from a laptop or a smartphone (Figures b and S2; Video clip S1). To reduce storage requirements without affecting phenotyping accuracy (Minervini et al ., ), images were encoded at the device using the lossless compression standard available in the PNG file format (although Phenotiki supports a variety of lossless and lossy image formats). At the end of the experiment, a ZIP archive containing all of the images acquired was automatically created on the Phenotiki device, and via the web‐based interface of the device we downloaded it to a local workstation for storage and processing (Figure S2).…”
Section: Methodsmentioning
confidence: 99%
“…WinRhizo ™ , appears to choose the threshold correctly, at least if images have a clear contrast, whereas IJ_Rhizo introduced large errors by misclassification. Our images contained minor variability in colour intensity (Supplement 3) as a result of image conversion (Minervini, Scharr, & Tsaftaris, ), which, nevertheless, introduced directional pixel misclassification towards less pixels being classified as root by IJ_Rhizo at 1,200 dpi but not 800 dpi (Supplement 3, zoom to see the effect). This explains the great overestimation of root length and underestimation of diameters we observed with automatic threshold at 1,200 dpi, since single root segments are partly interpreted as multiple very thin segments by IJ_Rhizo.…”
Section: Discussionmentioning
confidence: 99%
“…However, in practice, such data storage typically requires a lot of virtual memory resources. Importance of compression has also increased significantly due to online storage, sharing and transmission of images in different research areas (medical, agriculture, biomedical, engineering, biology and many more) [22][23][24]. In the field of medical imaging large number of images are produced for research, surgical applications and disease diagnostics.…”
Section: Introductionmentioning
confidence: 99%