The client, an agricultural research and development (R&D) organization, required a robust automated system for data collection, management, and analysis of crop seeds, for its parent company − a large US-based consumer products company.

 

Key Challenges

The crop seed breeding program requires specialized skills.

 

Data collected from multiple sources need to be analyzed quickly

 

Absence of a robust automated system for data collection, management, and analysis

 

Shorten the duration to create new variety.

The Impact

Robust data collection, management, and analysis methods

 

A secure and protected data environment

 

Reduction in errors caused by insufficient or inconsistent data

Line

The Solution

Creation the Agronomic Research Management System

 
   

Infosys developed analytical and statistical scripts / macros to enable the client to choose the best crop seed strains.

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A multi-disciplinary project team of domain consultants, technical architects, and data architects from Infosys worked with the client’s scientists and IT team to create the Agronomic Research Management System (ARMS), a Web-based solution using the Microsoft .NET technology, to address data management and analysis needs.

The analysis revealed that the client’s data could be viewed in two ways:

  • Recent – the most recent harvest (up to one year)
  • Historical – historical data which must be accessed and compared to recent information within a finite timeframe (i.e. put historical information in current context).

ARMS was designed to enable scientists to analyze data in ways they could not before. It provided unified access to a single source of data, a graphical interface for schematic layouts of field trials, and automated analysis of key parameters for selecting high-quality crop varieties.

Data architects worked with the IT team to define and develop the data model required for the project, and to facilitate the DQR process.

Reduced time-to-market

Lower costs due to automation

Improved maintainability, standardization, control, predictability, and traceability of data

Overall cost reduction

  • Data storage to facilitate regulatory compliance
  • Data storage practices that offer scalability and reduced testing rework
  • Improved maintainability, standardization, control, predictability, and traceability of data
  • Enhanced decision-making capabilities in choosing better variety of crop seed and better-quality end products
  • Better audit and control procedures.
  • Overall cost reduction due to streamlining of the traditional crop breeding process