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NIX helped a US agribusiness develop a mobile app to streamline their workflow by simplifying data collection across farming and packaging operations.
Agriculture
Mobile Development, Embedded Development
React Native, Raspberry Pi, BLE, C++, Python
Our client, a large-scale farming company that grows, packages, and sells various berries in the United States and Canada, faced challenges with tracking employee productivity and accurately monitoring the volume of ready-for-sale products. The existing manual processes were resource-intensive and prone to errors, which made it difficult for management to gauge how efficiently each worker was performing. These limitations also prevented the company from having real-time insights, which were crucial for making informed data-driven decisions on production and sales.
To address these issues, the client decided to digitalize their processes and make them more transparent through advanced technology. They turned to NIX with this request, choosing our team for our vast experience with complicated software development projects across domains, including agriculture. The main challenge was the very nature of the farming industry as most of the company’s employees work in the field without a stable internet connection or access to sophisticated devices.
Deliver a solution for field workers to record how many berries they’ve picked during their shift, ensuring process transparency, data accuracy, and automation of all the company’s operations.
Integrate this solution with a client scanner in a packaging factory to process data on product preparation for sale.
We developed a cross-platform React Native mobile app that can be used on the go with any smartphone and operate in any network and conditions, even offline. The application helps the company monitor the progress of field workers and integrates with a client’s scanner on the packaging line to track product preparation for sale.
The mobile app scans information from QR codes on boxes and employee badges and correlates the results with each other, thereby giving management an idea of how many berries each employee has collected. The system stores this data—including device ID, timezone, start and end date, grower ID, commodity grow type, etc.—in the device and sends it to the server if there is internet access.
An important project step was connecting the application to a scanner installed at the factory and equipped with a Raspberry Pi, a single-board computer. We did this through embedded development, creating C++ and Python-based software for the Raspberry Pi that controls the scanner and provides the ability to store all the data obtained within the device. Using BLE technologies, we integrated the created program with the mobile app, allowing them to exchange data via Bluetooth.
Our team also developed a special mechanism to bypass strict BLE limitations related to the maximum data packet size for a single transfer. This allows us to split a large amount of information on the Raspberry Pi side, encode the data to prevent its interception and transfer it in small packets to the mobile application. The system then checks the integrity of the delivered information and downloads the missing parts separately, if any, after which the packets are combined, decoded, and sent to the server.
The application has two modes of use: Field Packing and Inline Packing.
The farmer can use the application while working in the field, following this flow:
Employees also use this app on the factory’s packaging line to record the number of boxes of berries prepared for sale:
Our team added a feature that allows users to simultaneously scan and recognize several QR codes that fall into the smartphone camera. To implement this task, we used the Scandit library, which is an efficient solution when there is a need to scan a lot of code in one frame.
Our client received a robust business automation mobile solution that transformed and streamlined the company’s workflow by simplifying data collection across their farming and packaging operations.
Improved Performance Evaluation: Management receives precise, end-of-day data on each worker’s output, giving them reliable insights to evaluate staff performance through KPIs.
Enhanced Inventory Management: The system ensures accurate, real-time tracking of product availability, enabling informed decisions about inventory and sales forecasting.
Reduced Errors: Automating data entry minimizes human error and allows workers to focus on more essential tasks.
Seamless Offline Functionality: The system operates efficiently even in remote locations with no internet access, meeting the unique demands of the agriculture sector.
Streamlined Data Flow: The system manages data flow between mobile devices and on-site scanners, leveraging efficient cross-platform technology and smooth data integration.
30
minutes/day of work time saved per employee
50%
reduction in data entry errors
20%
reduction in training time
15%
increase in inventory forecast accuracy
We really care about project success. At the end of the day, happy clients watching how their application is making the end user’s experience and life better are the things that matter.
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