Smart sensing in Malawi

Case Study



Device assembly

Impact and partners

In partnership with the Centre for Agricultural Transformation (CAT) and funded by the Foundation for a Smoke-Free World (FSFW), we are working with private and public agribusiness partners to deploy and test GEMS Sensing technologies in a low-income, smallholder farmer, tropical environment.

Process and problem solved

We worked with a South African-based manufacturer to produce ~80 GEMS-designed sensor nodes for this pilot project. We are deploying and supporting these nodes on experimental research stations plus commercial and smallholder farms throughout Malawi to evaluate the system’s robustness and perceived value in Malawi.

GEMS Services Used

Fitting sensing technologies to sub-Saharan Africa

The sensing systems collect 8 parameters every 15 minutes: air temperature, humidity, barometric pressure, rainfall, soil moisture, soil temperature, wind speed, and solar radiation. Data are sent via the Airtel cell-phone network to the cloud, and are accessible via a user-friendly, web-based portal. Given geographical differences in cell-phone network coverage and strength, we are focused on evaluating the robustness of systems across a wide geographic area. Initial field-test results are encouraging. 

GEMS Sensing hardware was designed to reduce data collection costs while maintaining data quality standards. The pilot efforts are also designed to assess how best the new data streams enabled by these systems support real-world decisions in sub-Saharan Africa, be that technical choices made by agricultural scientists or management decisions made by smallholder and commercial farmers.  Through partnership with the Malawi CAT project, GEMS Sensing works with an array of agribusiness partners who are willing to help test that robustness and usefulness.


  • Identify partner data sensing needs and match those to GEMS Sensing capabilities
  • Compare data quality across different sensing deployment strategies
  • Use data to evaluate the precision and accuracy of spatially interpolated weather estimates obtained from existing sources 
  • Understand the total cost of ownership (i.e., costs of acquisition, installation, and maintenance) from deploying digital ag weather sensors in a low-income, tropical country
  • Introduce new technology to producers, input service providers, and other agribusinesses, such as downstream processors and aggregators, and evaluate their ability/willingness to pay