Problem: This crisis has a put a burden on food supply and now more than ever we need more people growing fresh food in local communities to relieve some stress on large farmers. The crisis has increased pressure on small farmers on developing countries who need free easy to use technology to improve yields.
The other problem is our society needs to be engaged in space science and there is nothing available in the market which brings that knowledge to over a billion social media users.
Solution: Our AR(augmented reality) instagram filters will use NASA GLAS datasets to use users geolocation to give them tips on which plant to grow in their gardens, allotments and farms. This will inspire people to learn about space science and create a network effect to enable everyday user to grow their own food. The by product of instagram filters could be analytics for Governments who can use this data to help local communities grow food by understanding how many users want to grow and which plant is most in demand.
Inspiration: Social networks, the internet and of course smartphones are today very powerful tools that we want to take advantage of to make life easier for people.
Augmented reality (AR) and the NASA datasets make this project possible. If everyone can see potential of food that can be grown in their area then people might be inspired to take action and start growing their food
Approach: First we tried to understand the format of NASA datasets and which datasets are applicable for this problem. We settled on NASA GLADS soil moisture dataset and Meteomatics agriculture data. We used python to probe the dataset and find out how we can use a person current location to determine nearest available soil moisture data. We tried saving the output as JSON file on server and then use Spark AR javascript scripting to develop an selfie filter which asks a user for gps permission and then gives them a text output based on moisture level. We built a website page, developed a name for business and created instagram page.
Challenges: Since this was our first time working with these datasets and never used SparkAR before it was a steep learning curve. We are not software developers so it meant staying up late to understand data. The biggest barrier was Spark AR recently disabled Networking module which meant we were unable to pull formatted data from Glitch servers. We had server issues with website as well which meant even though we had created website layout we were unable to publish it
You can find slides on our Github page (presentation) or through this link - presentation slides on google
NASA GLADS - soil moisture https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025_3H_2.0/summary Meteomatics - https://www.meteomatics.com/en/api/available-parameters/agricultural-parameters/