Reforms to agricultural and land management practices are needed to combat a future of soil exhaustion and food insecurity forecasted by scientific organizations worldwide, as the world’s populations continue to rise and issues of food security and nutrition are exacerbated.
Motivated by this call, we have been developing technologies to support agroecological analyses on a variety of different spatio-temporal scales.
Working as a technology partner for projects and organizations such as the Africa Soil Information Service (AfSIS), CIMMYT, Feed The Future, and One Acre Fund, QED has focused on developing end-to-end data processing workflows to support the full lifecycle of data. This workflow includes field data acquisition, barcoding, database management, and map visualization. These technologies have been introduced in several African countries in collaboration with their governments, with the aim of closing information gaps and positively influencing public policy and industrial practices, if executed in coordination with partners. Below we describe some of these technologies in further detail.
We have been developing technologies to allow scientists to more efficiently and reliably catalogue soil and landscape resources by leveraging some of today’s most influential technologies, including web and mobile, crowdsourcing, machine learning, cloud, and UAVs.
As an example, we can generate digital maps such as the one on the left by collecting crowdsourced assessments of recent satellite imagery backed by expert verification, and then building ML models to extrapolate predictions throughout the region. These predictions can also supplemented with uncertainty measures.
Readers are encouraged to browse more examples of applications below.
QED aims to build suites of interlocking software providing for end-to-end support. The components should be freely interchangeable, as our design is inspired by several key tenets of ESR’s rules of UNIX, reproduced below:
Leverage networks of satellites and UAVs to quickly scan regions of interest and generate maps of ecological indicators and DEMs.
Data is reliably stored on cloud infrastructure and replicated in multiple availability zones. Automated input validation of laboratory data.
Predictions of soil chemistry and land usage using machine learning executed on cloud infrastructure.
We express our thanks to our partners below in supporting the development of many of the tools above.
For more inquiries about our services or to request a demo, please e-mail: info@qed.ai