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IAPDissecting and modeling plant phenotypic components based on high-throughput image analysis
Dijun Chen & Christian Klukas


Plants reveal complex phenotypic traits which reflect both their architecture and physiological status. However, the multitude of phenotypic components is still largely unexplored. Here we provide a comprehensive framework for high-throughput phenotype data analysis in plants, which enables us to extract an extensive list of phenotypic traits from non-destructive plant imaging over time. To illustrate the investigative power of the developed methods we provide a example-dataset to illustrate the analysis of phenotypic components of drought responses of 18 different barley cultivars.

If you have any question, feel free to contact the developers of the presented methods by E-Mail: D. Chen, C. Klukas.

Reference Information

Dijun Chen, Kerstin Neumann, Swetlana Friedel, Benjamin Kilian, Ming Chen, Thomas Altmann, and Christian Klukas: Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis. Plant Cell tpc.114.129601 (2014) (link)

Acknowledgements for funding

This work was supported by IPK institute funds, and with grants supporting collaborations and business trips from the National Natural Science Foundation of China (NSFC, 31050110121), the Robert Bosch Stiftung (32.5.8003.0116.0), the Federal Agency for Agriculture and Food (BEL, 15/12-13, 530-06.01-BiKo CHN) and project funding of the Federal Ministry of Education and Research (BMBF) (OPTIMAL: 0315958A, DPPN: 031A053B), and the EU funded project EPPN (Grant Agreement No. 284443).

Barley Drought Stress Example Data (Numeric Analysis Results)

Experiment Setup

For this example, we applied our methodology to a high-throughput phenotyping on a barley population and produced a phenotypic map for barley plants from 18 genotypes (Supplementary Table 1) under control and drought-stress conditions over time. The 16 genotypes can be divided into three agronomic groups according to their breeding history: group 1 (released before 1950), group 2 (released between 1950 and 1990) and group 3 (released after 1990). The two DH cultivars are considered as an independent group: DH group. Per treatment 9 plants per genotype were investigated in the 16 German cultivars and 6 plants for the DH-parents during one experiment in May to July 2011. Plants grew under controlled greenhouse conditions and were phenotyped on a daily basis over the whole experimental phase using a fully automated system consisting of conveyer belts, a weighing and watering station and three imaging systems. Further background information will be added and linked in the very near future at this place.

Numeric Analysis Results (Download)

Numeric Analysis Results
. The image analysis result tables are available from this link.

For analysis purposes we used an open-source software, the Integrated Analysis Platform (IAP), that supports high-throughput image data management and processing, and various implemented algorithms that support efficient data analysis and mining and that help researchers to interpret the huge amount of phenotypic data.

Further analysis was performed using custom software written in R programing language (release 2.15.2). The R code is available to academic researchers upon request. Selected scripts which can be straight-forward used will be integrated into IAP (external scrip calls will be possible with IAP 2.0), selected scripts will also be later added to this page.

R post processing

Statistical Data Post-Processing

(Click to Download PDF)

38 pages

describes how to use the
analysis scripts

(Click to Download ZIP)

1087 lines of code

source code of the
anlysis scripts



Developed from 2010-2015 at IPK Gatersleben within the Group Image Analysis

Main development of the presented methods: D. Chen, supervision of the project and method development by C. Klukas (head of group), futher contributions to the method development, see reference paper.

Contact for questions: D. Chen and C. Klukas