Supplementary Fig. S1. System architecture for data collection and analysis.Data management consists of data collection, storage, and utilization. Data collection is to gather data of various control devices such as PC, CCTV, and image capturing machines. Control PC function in image acquisition and control of conveyor system. Priva PC control temperature and humidity of greenhouse. Imaging capturing machines (RGB, IR, NIR, and Fluorescence), CCTV, and Client PCs are stored in servers through interface like LAN and internet. Use of servers includes in data storage, and load of exported data in servers includes in data utilization. Control PC (intel I5-4570S, CPU 4, RAM 8 GB); Power Edge R420 (intel xeon E5-2470V2, CPU 4, RAM 32GB); power Edge R730 (intel xeon E5-2609V3, CPU 12, RAM 64GB); Power Edge R220 (intel xeon E3-2230V3, CPU 4, RAM 16GB); Power Edge MD1200 (intel xeon E5-2609V3, CPU 12, RAM 64GB).

Supplementary Table S1. Data management in detail

No. / Element / Descriptions
1. / Plan / MIT libraries (MIT) provided some questions that can be used to write data management plan:
  1. Project, experiment, and data description
What’s the purpose of the research?
What is the data? How and in what format will the data be collected? Is it numerical data, image data, text sequences, or modeling data?
How much data will be generated for this research?
How long will the data be collected and how often will it change?
Are you using data that someone else produced? If so, where is it from?
Who is responsible for managing the data? Who will ensure that the data management plan is carried out?
  1. Documentation, organization, and storage
Whatdocumentationwill you be creating in order to make the data understandable by other researchers?
Are you using metadata that is standard to your field? How will the metadata be managed and stored?
Whatfile formatswill be used? Do these formats conform to an open standard and/or are they proprietary?
Are you using a file format that is standard to your field? If not, how will you document the alternative you are using?
What directory andfile namingconvention will be used?
What are yourlocal storage and backup procedures? Will this data require secure storage?
What tools or software are required to read or view the data?
  1. Access, sharing, and re-use
Who has the right to manage this data? Is it the responsibility of the PI, student, lab, or funding agency?
What data will beshared, when, and how?
Does sharing the data raiseprivacy, ethical, or confidentiality concerns? Do you have a plan to protect or anonymize data, if needed?
Who holds intellectual property rights for the data and other information created by the project? Will any copyrighted or licensed material be used? Do you have permission to use/disseminate this material?
Are there any patent- or technology-licensing-related restrictions on data sharing associated with this grant?
Will this research be published in ajournalthat requires the underlying data to accompany articles?
Will there be any embargoes on the data?
Will youpermit re-use, redistribution, or the creation of new tools, services, data sets, or products (derivatives)? Will commercial use be allowed?
  1. Archiving
How will you be archiving the data? Will you be storing it in an archive or repository for long-term access? If not, how will you preserve access to the data?
How will you prepare data for preservation or data sharing? Will the data need to be anonymized or converted to more stable file formats?
Are software or tools needed to use the data? Will these be archived?
How long should the data to be retained? 3-5 years, 10 years, or forever?
2. / Process / -File version control. It is critical to keep track of versions of documents and datasets, including:
Directory structure naming conventions. Directory top-level folder should include the project title, unique identifier, and date. The substructure should have a clear and documented naming convention, such as numbering or naming the experiment runs, dataset versions, and/or researchers.
File naming conventions. Reserve the 3-letter file extension for application-specific codes, for example, formats like .wrl, .mov, and .tif. Identify the activity or project in the file name. Many disciplines have recommendations.
-Storage of data is typically done in an easily-accessible, secondary location. The data are typically mirrored, which means that the data in the secondary location is identical to the original version.
-Backup of data is typically done in a separate physical location that may be harder to access than regular storage space. Backups are snapshots of the information in your files at a given point in time. Usually only one version of the backup is kept, not multiple versions. Data backup can be done by make 3 copies (e.g. original + external/local + external/remote) and should be geographically distributed (local vs. remote). To make sure the backup system is working properly, test the system periodically. Try to retrieve data files and make sure that the data can be read.
-From an ethical standpoint, researchers should consider the implications of data ownership agreements before they are made with other researchers, institutions, or funding agencies.
-It is important for researchers to understand the relevant ownership rules for any data that they collect or use. From an ethical standpoint, researchers should consider the implications of data ownership agreements.
-Data must be archived in a controlled, secure environment in a way that safeguards the primary data, observations, or recordings. The archive must be accessible by scholars analyzing the data, and available to collaborators or others who have rights of access. Primary research data should be stored securely for sufficient time following publication, analysis, or termination of the project.
-Unencrypted security is ideal for data storing so that can easily read it. Encryption is required for sensitive data or personal information.
-Data documentation, also known as metadata, helps to understand data in detail, and also helps other researchers find, use, and properly cite your data. Various metadata standards are available for particular file formats and disciplines. Metadata makes it easier to identify and reuse data correctly at a later date
-Metadata is information about data, and describes basic characteristics of the data, such as: who created the data; what the data file contains; when, where, why, and how the data were generated, etc.
As technology changes, researchers should plan for both hardware and software obsolescence and consider the longevity of their file format choices to ensure long term readability and access. File formats more likely to be accessible in the future have the following characteristics: non-proprietary; open, documented standard; common usage by research community; standard representation (ASCII, Unicode); unencrypted; uncompressed.
-Consider migrating data into a format with the above characteristics, in addition to keeping a copy in the original software format. If data will be deposited in a repository, the files may be migrated to newer formats, so that it will be usable to future researchers.
-Choosing data formats and software depends mostly on the preference of the researcher but can often be dictated by discipline-specific standards and customs. While ensuring the long-term usability and sustainability of data requires attention to standard and interchangeable software
-Close attention to storage, back-up, security, and sustainability of data means lessen the risks of compromising their quality and accessibility over the long term. Issues related to storage include considering how rapidly data are expected to increase over the lifetime of the research project. Part of answering this question involves determining whether data will be collected in automated ways, which potentially steps up the scale of data collection, or whether staff on the project will be gathering data themselves (e.g., via inputting in a database, or a lab notebook). Options for short-term storage include hard disk drives and portable media
3. / Share / -Repositories can help: manage the data, cite data by supplying a persistent identifier, facilitate discovery of the data, and preserve data for the long-run.
-Many journals require that published articles be accompanied by the underlying research data. Data sharing policies often are found in the instructions for authors.
-Data sharing is often a natural part of the research process; however, funding agency may require sharing the data or making them publicly accessible. Before sharing, consider not only the metadata you will need to provide along with the data to make it easily understood, but also the privacy, intellectual property, copyright, or licensing issues to be addressed with regard to the sharing.

Supplementary Table S2.Image analysis programs and phenotyping platforms (Cobb et al. 2013; Fiorani et al. 2013).

Plant tissue / Software / Purpose / References
Roots / WinRhizo Tron / Root area, volume, length /
Root reader 3D / Root system architecture / Clark et al. (2011)
EZ-Rhizo / Root system architecture / Armengaud et al. (2009)
RootTrace / Counting and measuring root morphology / Naeem et al. (2011),
French et al. (2009)
DART / Root system architecture / Le Bot et al. (2010)
SmartRoot / Quantification of growth and architecture in root / Lobet et al. (2011)
Gia-Roots / Root system architecture / Galkovskyi et al. (2012)
Leaves / TraitMill / Measurement for various agronomic characteristics / Reuzeau et al. (2006)
PHENOPSIS / Measurement of water deficit-related traits / Granier et al. (2006)
LeafAnalyser / Rapid analysis of leaf shape / Weight et al. (2007)
HTPheno / Measurement for various shoot characteristics / Hartmann et al. (2011)
LemnaTec 3D Scanalyzer / Leaf color, shape, size, and architecture / Golzarian et al. (2011),
Seeds / WinSEEDLE / Volume and surface area measurements of seeds /
products/needle/WinSEEDLE.html
SHAPE / Measurement of seed shape / Iwata and Ukai (2002),
Iwata et al. (2010)
SmartGrain / Measurement of seed shape / Tanabata et al. (2012)