J. Plant Biol. (2017) 60:285-297 DOI 10.1007/s12374-017-0027-x Data Management for Plant Phenomics Song-Lim Kim † , Nita Solehati † , In-Chan Choi, Kyung-Hwan Kim and Taek-Ryoun Kwon* The National Institute of Agricultural Sciences, 370 Nongsaengmyeong-ro, Wansan-gu, Jeonju-si, Jeollabuk-do 55365, Korea Received: January 19, 2017 / Accepted: March 27, 2017 © Korean Society of Plant Biologists 2017 Abstract Plant phenomics is an area of biology dealing with the analysis of phenotypic traits in plants. It can be co- integrated 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. Keywords: Data management, Phenotypic traits, Plant phenomics Introduction Agricultural production is facing enormous challenges especially to meet the growing demand of food supply for continuously growing world population. The United Nation estimates that agricultural production has to increase 70% more globally by next 35 years to meet the food demand (Ray et al. 2015). The Food and Agriculture Organization (FAO) noted that some developing countries in Asia and the Pacific need to increase their food production by up to 77 per cent to feed their people by 2050, when the world’s population is expected to top nine billion (Ye et al. 2016). Plant phenomics is the study of plant growth, performance, and composition. Forward phenomics uses phenotyping tools to ‘sieve’ collections of germplasm with target traits. It can include the tolerance against abiotic or biotic stress. Reverse phenomics is the detailed dissection of traits shown to be of value to reveal fundamental understanding and allow exploitation of a mechanism in new approaches. This can involve reduction of a physiological trait to biochemical or biophysical processes and ultimately a gene or genes (Furbank et al. 2011). Phenomics is a fundamental science that enables high-throughput phenotyping for crop improvement in response to demographic and climate scenarios (Houle et al. 2010; Yang et al. 2013). Phenomics of crop plant is based on the enormous data of plant phenotyping collected via high- throughput system such as morphological characteristics (plant height, leaf area, fruit number and so on), physiological traits (water use efficiency, photosynthetic activities and so on), and biochemical traits (anthocyanin content) (Gago et al. 2014; Garriga et al. 2014; Paulus et al. 2014; Rahaman et al. 2015). Phenomics is adopting multidiscipline technologies consisted of hardware and software (Li et al. 2014; Lin 2015). There are many investments in phenomics from various countries, such as America, Australia, Belgium, England, France, Germany, Japan, China, India, and Korea. Those countries set facilities of high-throughput plant phenotyping with visible (VIS), near-infrared (NIR), infrared (IR), and/or hyperspectral images. The facilities were mainly to analyze grains (rice, wheat, and corns), vegetables (tomato), and fruits. There are various analysis of phenotypic traits, such as biomass (Menzel et al. 2009; Golzarian et al. 2011), root REVIEW ARTICLE These authors contributed equally. *Corresponding author; Taek-Ryoun Kwon Tel : +82-63-238-4661 E-mail : trkwon@Korea.kr