Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping
Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping
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DOI：
10.1104/pp.113.233932
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发表时间：
2014-06-01
期刊：
影响因子：
7.4
通讯作者：
Pape, Jean-Michel
中科院分区：
文献类型：
--
作者：
Klukas, Christian;Chen, Dijun;Pape, Jean-Michel
高通量表型分析技术是研究植物表型组分的重要技术。高效的图像处理和特征提取是基于表型性状量化植物生长和性能的先决条件。问题包括大规模表型数据集的数据管理、图像分析和结果可视化。在这里，我们提出了综合分析平台（IAP），一个高通量植物表型分析的开源框架。IAP提供了用户友好的界面，其核心功能具有很强的适应性。我们的系统支持图像数据传输从不同的采集环境和大规模的图像分析不同的植物物种的实时成像数据从不同的光谱。由于要管理的数据量巨大，我们使用了一个通用的数据结构来有效地存储和组织输入数据和结果数据。我们实现了一个基于块的方法自动图像处理，以提取植物表型性状的代表性列表。我们还提供内置数据绘图和结果导出工具。为了验证IAP，我们进行了一个包含33个玉米（Zea mays 'Fernandez'）植物的示例实验，这些植物在具有非破坏性成像的自动化温室中生长9周。随后，图像数据进行了自动化分析与玉米管道在我们的系统中实现。我们发现，计算出的数字体积和叶片数与我们的手动测量的数据在高精度高达0.98和0.95，分别。总之，IAP提供了用于导入/导出、管理和自动化分析高通量植物表型数据的多组功能，并且其分析结果高度可靠。
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.