Read more about the prototype tool we published as part of our ongoing work to improve the use of environmental data in agricultural research.

In recent years, the fusion of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) has opened up unprecedented opportunities for agricultural innovation.

Fertilizers are essential for boosting crop yields and increasing farm productivity. However, excessive use of fertilizers not only incurs high costs for farmers but also leads to harmful runoff that pollutes waterways.

One of the big frustrations in using computing to solve large, multidisciplinary challenges is managing data sets from different disciplines.

We are delighted to announce that we have just released the GEMS Grid code library, where the code is under the open source Apache 2.0 license.

Assessing BMP Effectiveness for Water Quality Outcomes with Remote Sensing.

GEMS Sensing has partnered with the Centre for Agricultural Transformation (CAT) in Malawi to pilot the local deployment of GEMS research-grade weather stations and its associated data visualization and sharing tool.