Automated farm data coordination system.



Obsolete data management system prone to human-made errors and unrecoverable data losses.



Development of robust, scalable software for optimized farm data management.

Tech stack

Tech stack

Python, PostgreSQL, Apache Hadoop, Angular, AWS, Docker.


Our partner is a medium-sized commercial farm located in Central Europe specializing in providing the EU customers with organic vegetable and dairy products. With a growing demand for their products, our partner sought to modernize outdated data management systems by hiring experienced Agtech software developers at Modsen.
Agricultural management software


The farm produce quality is directly linked to the accuracy of data management at every stage of the production process. Post-harvest handling was negatively impacted by semi-paper-based data storing, the absence of an integrated process, loss of archived data, and complexity of its analysis, leading to inaccurate business insights. With the farm's expanding operations, there was an urgent need for streamlined data processing to optimize productivity and ensure compliance with industry standards. Our partner turned to us in search of seasoned Agtech engineers capable of building a custom agricultural data management automation system that would help the farm owner:

  • Calculate production costs and record inventory.
  • Minimize errors and streamline data collection and analysis.
  • Compare economic indicators of different crops.
  • Optimize the cost of agricultural produce.
  • Improve after-sales client servicing.
  • Track crop yields and livestock data.
  • Effectively manage the inventory.



Full-Stack Engineers


QA testers


Data Analysts


Project Manager


Team Lead


UI/UX Designers

Modsen engineer

Development process

  • Requirements gathering and processing

    To understand the unique requirements of our client's project, we conducted an on-site visit followed by 3 online meetings with the farm's management and staff to comprehend their data management challenges. Such close interactions allowed us to document a detailed set of requirements and specificities, ensuring that our farm data automation solution would address every roadblock our partner faces.

  • Planning

    With the gathered requirements at hand, our project planning phase commenced. As always, we adhered to Agile methodologies to create a comprehensive software development roadmap, which included project milestones, allocated resources, key priorities, and deadlines. Regular meetings with the client ensured that the final plan was aligned with their goals and expectations.

  • Team assembly

    Bespoke Agtech software development required a team of skilled and expertised engineers having a strong background in building digital agriculture solutions. Our meticulous recruitment process included technical assessments, client tech interviews, and domain-specific experience evaluations. Given the specialized nature of the project, we engaged candidates who could provide maximum value to the custom agricultural software development process.

  • Design

    Intuitiveness and navigability of the farm data management system was the key priority for the client. Modsen Design Studio specialists combined UX simplicity with functionality, ensuring that farm staff could easily input, access, and interact with the data. The design phase involved UX research, strategy building, wireframing, and prototyping, based on continuous feedback from the client to fine-tune the system's look and feel.

  • Coding

    Leveraging the finest Agtech solutions and technologies, our development team created a robust and scalable agricultural data management software for farm productivity optimization. We built a web-based platform that allowed real-time data entry, analysis, and reporting. The system was integrated with sensors and IoT devices deployed on the farm, enabling automated data collection and analysis.

  • Comprehensive testing

    To polish the developed product and ensure the reliability and accuracy of the system, our QA team conducted a thorough testing that encompassed functionality testing, data validation, performance, and usability testing. The identified bugs were documented and patched by the developer team, with retesting conducted to validate the fixes.

  • Integration

    Upon successful development and testing, we transferred the code, tech documentation, and user guidelines, and integrated the data management system into the farm's existing infrastructure, which involved connecting the platform to data sources such as sensors, databases, and farm equipment. Our team also provided online training to farm personnel to make sure the software product would be utilized to the fullest and would soon bear its fruit.

  • Servicing and further cooperation

    Our commitment to a long-term partnership with the client was seen through prolonged technical support and maintenance, regular updates, and improvements introduced to the software, based on user feedback and evolving farm requirements. To date, we’ve implemented 2 farm management software scaling projects and keeping in touch with our partners to provide further tech assistance.


The implementation of our custom data management system brought our client's farming operations to a brand new level of efficiency. Key outcomes included the following positives:

  • Streamlined data collection and analysis improved decision-making processes.
  • Automation reduced manual data entry tasks, allowing farm staff to focus on core activities.
  • The system provided valuable insights into crop and livestock performance, enabling proactive management.
  • The farm met industry regulations and quality standards with ease, enhancing its reputation.
  • With optimized operations, the agricultural business experienced steady growth in production and revenue.

The partnership between our software development team and the commercial farm continues to thrive, with ongoing enhancements and adaptations to meet the evolving needs of the agricultural industry.


Boost in conducting the most complex and time-consuming processes


Growth of the quantity of certain crops


Average decrease in production costs due to data insights

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