Oral Wilson
@DeepAgronomyLabs

We are a research and development team consisting of PhD graduates in Chemistry, Energy and Agriculture.

Our overall mission is to develop a digital ecosystem to drive investment in agriculture in developing nations in order to mitigate the impacts of climate change on food resources.

To achieve this mission we are focused on two main goals.

1. The development of a machine learning powered decision support system geared towards providing actionable solutions to farmers and collect data on the state of agriculture at the local level primarily relying on image processing instead of expensive sensor data. With a platform capable of collecting this detailed data we will be able to push for more investment into agriculture lifting a generation out of poverty.

2. Our second project is focused on the development of the agrovoltaics industry. This is becoming a very popular area of research for large corporations but we risk farmers being left out of the ownership structure of these solar farms. We are working on the prototyping of a low cost organic photovoltaic system that can replace greenhouse technology and play a major role in dual land use. These systems can be tuned to target parts of the solar spectrum that won't reduce plant growth while reducing the water consumption on farms especially in sunny arid regions.

Together these two projects will provide an opportunity to digitize and monitor farms in far off remote regions of the world in an aim to increase production of food and green electricity on the minimal amount of land possible.

Use of funds:

1. Active development of simple to use mobile app for farmers aiming for compatibility with older and low cost devices.

2. Development of our machine learning algorithms using public research data from around the world.

3. Procuring computational resources to prototype new machine learning algorithms to optimize farming best practices as well as optimize our organic solar module design.

4. Testing our combined projects under different climatic conditions.