Supporting data for "Predicting plant biomass accumulation from image-derived parameters"

Dataset type: Imaging, Software
Data released on January 08, 2018

Chen D; Shi R; Pape JM; Neumann K; Arend D; Graner A; Chen M; Klukas C (2018): Supporting data for "Predicting plant biomass accumulation from image-derived parameters" GigaScience Database. http://dx.doi.org/10.5524/100392

DOI10.5524/100392


Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologist. However, it is a great challenge to find a predictive biomass model across experiments.
In the present study, we constructed four predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to three consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieve the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high cross experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of plant biomass outcome. Furthermore, the methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass.
We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species.

Additional details

Read the peer-reviewed publication(s):

Chen, D., Shi, R., Pape, J.-M., Neumann, K., Arend, D., Graner, A., … Klukas, C. (2018). Predicting plant biomass accumulation from image-derived parameters. GigaScience, 7(2). doi:10.1093/gigascience/giy001

Additional information:

https://github.com/htpmod/HTPmod





Sample IDTaxonomic IDCommon NameGenbank NameScientific NameSample Attributes
1121KN0914513barley Hordeum vulgare Description:Barley cultivar BCC_1433_HeilsFranken ...
Cultivar:BCC_1433_HeilsFranken
Perturbation:control
...
+
1121KN0924513barley Hordeum vulgare Description:Barley cultivar BCC_1433_HeilsFranken ...
Cultivar:BCC_1433_HeilsFranken
Perturbation:control
...
+
1121KN0934513barley Hordeum vulgare Description:Barley cultivar BCC_1433_HeilsFranken ...
Cultivar:BCC_1433_HeilsFranken
Perturbation:control
...
+
1121KN0944513barley Hordeum vulgare Description:Barley cultivar BCC_1433_HeilsFranken ...
Cultivar:BCC_1433_HeilsFranken
Perturbation:control
...
+
1121KN0954513barley Hordeum vulgare Description:Barley cultivar BCC_1433_HeilsFranken ...
Cultivar:BCC_1433_HeilsFranken
Perturbation:control
...
+
1121KN0964513barley Hordeum vulgare Description:Barley cultivar BCC_1433_HeilsFranken ...
Cultivar:BCC_1433_HeilsFranken
Perturbation:control
...
+
1121KN0974513barley Hordeum vulgare Description:Barley cultivar BCC_1433_HeilsFranken ...
Cultivar:BCC_1433_HeilsFranken
Perturbation:control
...
+
1121KN0984513barley Hordeum vulgare Description:Barley cultivar BCC_1433_HeilsFranken ...
Cultivar:BCC_1433_HeilsFranken
Perturbation:control
...
+
1121KN0994513barley Hordeum vulgare Description:Barley cultivar BCC_1433_HeilsFranken ...
Cultivar:BCC_1433_HeilsFranken
Perturbation:control
...
+
1121KN1004513barley Hordeum vulgare Description:Barley cultivar BCC_1433_HeilsFranken ...
Cultivar:BCC_1433_HeilsFranken
Perturbation:stress
...
+
Displaying 91-100 of 936 Sample(s).




File NameSample IDData TypeFile FormatSizeRelease Date 
GitHub archivearchive651.09 KB2017-12-28
1137KN001 +imageTAR86.29 MB2017-12-28
ReadmeTEXT3.11 KB2017-12-28
External linkUNKNOWN555.8 GB2017-12-28
Tabular DataUNKNOWN128.22 KB2017-12-28
External linkUNKNOWN540.1 GB2017-12-28
External linkUNKNOWN327.4 GB2017-12-28
Tabular DataUNKNOWN128.37 KB2017-12-28
Tabular DataUNKNOWN126.62 KB2017-12-28
Displaying 1-9 of 9 File(s).
Funding body Awardee Award ID Comments
Robert Bosch Stiftung C Klukas 32.5.8003.0116.0
Federal Agency for Agriculture and Food C Klukas 15/12-13 530-06.01-BiKo CHN
Bundesministerium für Bildung und Forschung C Klukas 0315958A
Bundesministerium für Bildung und Forschung C Klukas 031A053B
European Plant Phenotyping Network C Klukas 284443
Date Action
January 8, 2018 Dataset publish
March 30, 2018 Manuscript Link added : 10.1093/gigascience/giy001