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Case Study | Agriculture

Data Modeling Provides More Efficient, User-friendly Solution

Our client develops data driven technologies that help farmers run efficient operations to produce more food. The company—servicing millions of acres worldwide—uses innovative digital agronomic tools to focus on the sustainable production of high-yield, high-quality crops. The solutions are designed to optimize inputs, minimize environmental impact, and protect the farm’s economic viability.

Business Challenge

In order to address the constant need for improvement, the client wanted to update its existing tool to:

  • Optimize modules
  • Improve code readability
  • Provide an improved maintenance experience

The goal of these changes was aimed at providing a more efficient and user-friendly product for the client’s customers.

Project Description

The client consulted with the SoftServe team to work on the back-end of its web application, which analyzes initial ground field data including humidity, crop capacity, speed the machines cultivate the field, how the crop is growing, and amount of fertilizers. The amount of the analyzed data varies between 40-60 TB. After the mathematic calculations the system sends a report, recommendations, and profit prognosis. The analyzed data is released in three different formats: PDF, HTML, and API. This generated data is not stored because there is no internal database storage. For unit measurements, the program uses two systems—internationally adopted metric system and imperial system. The metric system is automatically predefined according to geolocation. It can also be set up manually.

End users of the application are either client customers—farmers—or other services and tools that are tightly connected.

The SoftServe Kanban team—two senior Python developers, a junior python developer, and ATQC—integrated with the client’s team, and a tech business analyst for some tasks. The whole data modeling team works on an internal app for client’s platform. The requirements were defined by the product owners (POs). The estimated time for implementation varied—two days to two months, depending on task complexity.

The team focused on three main tasks:

  • Data analysis and processing optimization and architecture improvements to achieve cost savings
  • New feature Introductions—comparatively rare activity
  • Bug fixing

During the project, the team faced several challenges including a lack of project documentation and the need for refactoring. Also, the architecture diagram was inferior and it was difficult to predict how one developed part would influence the system or module bundle.

Technology Stack

Python Selenium PyCharm Confluence
NumPy Celery Git ElasticSearch
SciPy Cython Jenkins RabbitMQ
Reportlab Resterio Pyunit Qgis
Matplotlib Gdal GitHub ElFinder
Django Shapely cProfiler AWS
Redis Azure Jira

Value Delivered

As a result of the collaboration, SoftServe helped the client update its technologies to remain competitive in the market. Process optimization was achieved through the collaboration between the two parts and integration of ATQC expertise into the development process. The updated product will help the client meet the needs of existing customers as well as new customer engagements.

The trust created with this collaboration enabled SoftServe to become a reliable partner for additional project implementations.