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HTPheno: an image analysis pipeline for high-throughput plant phenotyping.

Hartmann A, Czauderna T, Hoffmann R, Stein N, Schreiber F - BMC Bioinformatics (2011)

Bottom Line: It provides the possibility to analyse colour images of plants which are taken in two different views (top view and side view) during a screening.Within the analysis different phenotypical parameters for each plant such as height, width and projected shoot area of the plants are calculated for the duration of the screening.HTPheno is applied to analyse two barley cultivars.

View Article: PubMed Central - HTML - PubMed

Affiliation: Leibniz Institute of Plant Genetics and Crop Plant Research, Corrensstrasse 3, 06466 Gatersleben, Germany.

ABSTRACT

Background: In the last few years high-throughput analysis methods have become state-of-the-art in the life sciences. One of the latest developments is automated greenhouse systems for high-throughput plant phenotyping. Such systems allow the non-destructive screening of plants over a period of time by means of image acquisition techniques. During such screening different images of each plant are recorded and must be analysed by applying sophisticated image analysis algorithms.

Results: This paper presents an image analysis pipeline (HTPheno) for high-throughput plant phenotyping. HTPheno is implemented as a plugin for ImageJ, an open source image processing software. It provides the possibility to analyse colour images of plants which are taken in two different views (top view and side view) during a screening. Within the analysis different phenotypical parameters for each plant such as height, width and projected shoot area of the plants are calculated for the duration of the screening. HTPheno is applied to analyse two barley cultivars.

Conclusions: HTPheno, an open source image analysis pipeline, supplies a flexible and adaptable ImageJ plugin which can be used for automated image analysis in high-throughput plant phenotyping and therefore to derive new biological insights, such as determination of fitness.

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Validation of HTPheno. Two exemplary correlations (A, E) and the deviation of automatically obtained values from manually measured values are plotted. The values have been manually derived from the images of 8 plants: top view (A, B, C, D), side view (E, F, G) at 6 different days after sowing. The colours in the correlation diagrams (A, E) represents 6 different time points after sowing. Every bar in the deviation diagrams shows the mean deviation of the automatically obtained values from the manually obtained values for these 8 plants as black line and the interquartile range between the lower quartile and the upper quartile in green which indicate the distribution of 50 percent of the samples. Additionally the range (minimal and maximal parameter values) is given as black horizontal line.
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Figure 4: Validation of HTPheno. Two exemplary correlations (A, E) and the deviation of automatically obtained values from manually measured values are plotted. The values have been manually derived from the images of 8 plants: top view (A, B, C, D), side view (E, F, G) at 6 different days after sowing. The colours in the correlation diagrams (A, E) represents 6 different time points after sowing. Every bar in the deviation diagrams shows the mean deviation of the automatically obtained values from the manually obtained values for these 8 plants as black line and the interquartile range between the lower quartile and the upper quartile in green which indicate the distribution of 50 percent of the samples. Additionally the range (minimal and maximal parameter values) is given as black horizontal line.

Mentions: To validate the results produced by the HTPheno plugin images of 8 plants were chosen from an experiment period which started at day 28 after sowing and ended at day 54 after sowing. Different parameters were measured manually for comparison with the obtained parameters from the HTPheno plugin (see Figure 4). The x-extent values in top view images and the width values in side view images obtained by HTPheno correlate strongly with the manually measured x-extent values and width values (see Figure 4A, E).


HTPheno: an image analysis pipeline for high-throughput plant phenotyping.

Hartmann A, Czauderna T, Hoffmann R, Stein N, Schreiber F - BMC Bioinformatics (2011)

Validation of HTPheno. Two exemplary correlations (A, E) and the deviation of automatically obtained values from manually measured values are plotted. The values have been manually derived from the images of 8 plants: top view (A, B, C, D), side view (E, F, G) at 6 different days after sowing. The colours in the correlation diagrams (A, E) represents 6 different time points after sowing. Every bar in the deviation diagrams shows the mean deviation of the automatically obtained values from the manually obtained values for these 8 plants as black line and the interquartile range between the lower quartile and the upper quartile in green which indicate the distribution of 50 percent of the samples. Additionally the range (minimal and maximal parameter values) is given as black horizontal line.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3113939&req=5

Figure 4: Validation of HTPheno. Two exemplary correlations (A, E) and the deviation of automatically obtained values from manually measured values are plotted. The values have been manually derived from the images of 8 plants: top view (A, B, C, D), side view (E, F, G) at 6 different days after sowing. The colours in the correlation diagrams (A, E) represents 6 different time points after sowing. Every bar in the deviation diagrams shows the mean deviation of the automatically obtained values from the manually obtained values for these 8 plants as black line and the interquartile range between the lower quartile and the upper quartile in green which indicate the distribution of 50 percent of the samples. Additionally the range (minimal and maximal parameter values) is given as black horizontal line.
Mentions: To validate the results produced by the HTPheno plugin images of 8 plants were chosen from an experiment period which started at day 28 after sowing and ended at day 54 after sowing. Different parameters were measured manually for comparison with the obtained parameters from the HTPheno plugin (see Figure 4). The x-extent values in top view images and the width values in side view images obtained by HTPheno correlate strongly with the manually measured x-extent values and width values (see Figure 4A, E).

Bottom Line: It provides the possibility to analyse colour images of plants which are taken in two different views (top view and side view) during a screening.Within the analysis different phenotypical parameters for each plant such as height, width and projected shoot area of the plants are calculated for the duration of the screening.HTPheno is applied to analyse two barley cultivars.

View Article: PubMed Central - HTML - PubMed

Affiliation: Leibniz Institute of Plant Genetics and Crop Plant Research, Corrensstrasse 3, 06466 Gatersleben, Germany.

ABSTRACT

Background: In the last few years high-throughput analysis methods have become state-of-the-art in the life sciences. One of the latest developments is automated greenhouse systems for high-throughput plant phenotyping. Such systems allow the non-destructive screening of plants over a period of time by means of image acquisition techniques. During such screening different images of each plant are recorded and must be analysed by applying sophisticated image analysis algorithms.

Results: This paper presents an image analysis pipeline (HTPheno) for high-throughput plant phenotyping. HTPheno is implemented as a plugin for ImageJ, an open source image processing software. It provides the possibility to analyse colour images of plants which are taken in two different views (top view and side view) during a screening. Within the analysis different phenotypical parameters for each plant such as height, width and projected shoot area of the plants are calculated for the duration of the screening. HTPheno is applied to analyse two barley cultivars.

Conclusions: HTPheno, an open source image analysis pipeline, supplies a flexible and adaptable ImageJ plugin which can be used for automated image analysis in high-throughput plant phenotyping and therefore to derive new biological insights, such as determination of fitness.

Show MeSH