An open-source information system for high-throughput plant phenotyping enables large-scale image analysis for different species based on real-time imaging data obtained from different spectra. High-throughput phenotyping is emerging as an important technology to dissect phenotypic components in plants. Efficient image processing and feature extraction are prerequisites to quantify plant growth and performance based on phenotypic traits. Issues include data management, image analysis, and result visualization of large-scale phenotypic data sets. Here, we present Integrated Analysis Platform (IAP), an open-source framework for high-throughput plant phenotyping. IAP provides user-friendly interfaces, and its core functions are highly adaptable. Our system supports image data transfer from different acquisition environments and large-scale image analysis for different plant species based on real-time imaging data obtained from different spectra. Due to the huge amount of data to manage, we utilized a common data structure for efficient storage and organization of data for both input data and result data. We implemented a block-based method for automated image processing to extract a representative list of plant phenotypic traits. We also provide tools for build-in data plotting and result export. For validation of IAP, we performed an example experiment that contains 33 maize (Zea mays ‘Fernandez’) plants, which were grown for 9 weeks in an automated greenhouse with nondestructive imaging. Subsequently, the image data were subjected to automated analysis with the maize pipeline implemented in our system. We found that the computed digital volume and number of leaves correlate with our manually measured data in high accuracy up to 0.98 and 0.95, respectively. In summary, IAP provides a multiple set of functionalities for import/export, management, and automated analysis of high-throughput plant phenotyping data, and its analysis results are highly reliable.
Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping1[C][W][OPEN]
Christian Klukas,Dijun Chen,Jean-Michel Pape
Published 2014 in Plant Physiology
ABSTRACT
PUBLICATION RECORD
- Publication year
2014
- Venue
Plant Physiology
- Publication date
2014-04-23
- Fields of study
Biology, Computer Science, Engineering, Environmental Science, Medicine
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
CITATION MAP
EXTRACTION MAP
CLAIMS
CONCEPTS
- block-based image processing
An automated image-processing approach that operates on image blocks to extract plant traits.
Aliases: block-based method, block processing
- common data structure
A shared data organization used to store and handle both input images and derived results in the platform.
Aliases: shared data structure, unified data structure
- digital volume
A computed phenotypic volume measure derived from the image analysis workflow.
Aliases: computed digital volume
- high-throughput plant phenotyping
Large-scale measurement of plant traits from imaging data to quantify growth and performance.
Aliases: HTP, plant phenotyping
- integrated analysis platform (iap)
An open-source software framework presented for managing and analyzing high-throughput plant phenotyping data.
Aliases: IAP
- maize pipeline
The maize-specific analysis workflow implemented in the platform for processing imaging data from Zea mays.
Aliases: maize analysis pipeline
- number of leaves
A leaf-count phenotypic measure extracted from the maize imaging analysis.
Aliases: leaf number, leaf count
REFERENCES
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