Data Management for Plant Phenomics
2017
Kim, S.L., The National Institute of Agricultural Sciences, Jeonju, Republic of Korea | Nita Solehati, The National Institute of Agricultural Sciences, Jeonju, Republic of Korea | Choi, I.C., The National Institute of Agricultural Sciences, Jeonju, Republic of Korea | Kim, K.H., The National Institute of Agricultural Sciences, Jeonju, Republic of Korea | Kwon, T.R., The National Institute of Agricultural Sciences, Jeonju, Republic of Korea
Plant phenomics is an area of biology dealing with the analysis of phenotypic traits in plants. It can be cointegrated with other omics like functional genomics, transcriptomics, and metabolomics etc. Phenotypic traits are generated by images of RGB, hyperspectral, near-infrared, thermal, fluorescence imaging and so on. Characterized phenotypes can be revealed in various morphological and physiological measurements of size, growth pattern, biomass and color in plants. The image-base automated plant phenotyping is described as a high throughput plant facility. Despite its advantages like nondestructive phenotyping it has its own limitations such as plant's complex architectures and environmental conditions at the time of image capture especially in the field. Phenomics generates a large number of images and metadata through phenotyping instruments, so there is a need for proper data processing and managements. Standardized data storage and sharing is also necessary for meaningful data acquisition along with statistical analysis. Processes of data management are largely consisted of data collection, storage, documentation, along with improvement of data quality. In future, plant phenomics must be developed efficiently to store, analyze, protect and share the acquired data. Modern high throughput plant phenotyping could be used effectively in plant improvement programs.
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