Gaia | Devpost

Gaia (Logo)

TL; DR

The climate crisis is one that spans across many spheres of our society: solving it starts with us, with Gaia! DIY agriculture is an effective way of promoting carbon sequestration! Reducing trips to the grocery lowers carbon emission! Double Whammy! Gaia implements ML classification for personalized crop recommendation! Connect with fellow farmers; let’s work together!

“Hey, what can we do???” - Anderson .Paak

We don’t have time to wait on big businesses to address their role in the worsening global climate crisis. Until they do, we must focus on what we can do.

According to the International Energy Agency, CO2 emissions from oil alone grew by 2.5% between 2015 and 2022. Oceans are warming, ice caps are melting, sea levels are rising, and rainfall is becoming more unpredictable.

The United Nations have suggested a range of lifestyle changes to address this pervasive issue, such as reducing food waste, reducing, reusing, and recycling, or even purchasing entirely new vehicles (EVs).

For us, there was one interesting suggestion that they made that helped inspire our project: “eat more vegetables”. We thought to ourselves, well, maybe we could grow them first? https://devpost.com/software/541304/joins/_4z5WdpiHbY_jNXnbc2itw

The root of a solution: Gaia

High tech solutions to this problem are steadily emerging, but Gaia simplifies the act of growing, selling, and purchasing our own produce. We used a K nearest neighbors ML classification algorithm that delivers information to users about which crops would thrive most fruitfully in their place of residence. Gaia makes use of agricultural metadata to make it easier for users to take a different approach to combating the climate crisis.

And hopefully, it helps users make a few friends along the way.

Gaia’s Greener Generation

As mentioned before, we know that the climate crisis is highly interwoven in the workings of modern society. Watch any documentary about the direction our Earth’s going, and you might be slightly overwhelmed. For large scale issues like these, it was important for us to first look inward, asking ourselves what we could control. As LA Hacks neared, we looked around, noticing that we’re not alone in this fight.

Remembering the relatively under-advertised greenhouses that were present at each of our high schools, we saw that growing our own produce could be a cost-effective – even enjoyable – alternative to donating $100,000+ to Elon Musk.

We come from a variety of backgrounds: Russell, a Neuroscience major (with less programming experience) interested in the obstacles that our generation faces, looked into the literature to find climate trends that Gaia aims to remediate.

Jeff, Pranav, and Bach are computer scientists. Jeff and Bach used React Native on the frontend. Pranav handled the backend with Flask and Firebase, implementing a K Nearest Neighbors ML algorithm to make crop recommendations to users and using string hashing techniques to enable secure payment processing.

Any user that downloads Gaia will experience quick, concise information about what crops will thrive in their residential zones, from cabbage to carrots. Once a user chooses their crops of interest, they are provided with advice on how to nurture them properly. Finally, users can monitor the growth of their crops with a feature that tracks crops’ progress.

Gaia, by easing the process through which users can learn how to grow their own produce, makes it easier for anyone to have an active role in combating climate change.

Challenges to growth

Dynamic components that changed and are updated frequently with user interaction posed challenges along the way. Updating the app’s interface based on the preferences and activities of the user.

Integrating the front and backend development presented minor challenges towards the end of Gaia’s creation.

Databases that were more familiar to us were incompatible with this project. Learning new technologies that revolved around backend development.

What worked

Maintaining two GitHub repositories simultaneously allowed for efficient code modification from different angles

Relying on past experience in creating a project in a tight timeframe allowed us to work well with one another

Lessons learned

Extracting from agricultural metadata of different forms will take time

Some plants/crops (like an apple tree) take awhile grow; tailoring suggestions to each user based on how quickly they want their crops is something to take into account

Sleep is important for productive programming and teamwork…

Expediting a better tomorrow, together

(implications, steps forward)

With the ML classification algorithm used in Gaia, the app congregates those who are committed to self-sustainability. In the future we want to apply other algorithms that would recommend “Growing Groups” that looks at metadata about geographic location and recommends users to work together in clusters.

We also want to look into partnering with local businesses across the nation with a similar mission as Gaia: this method of combating climate change is certainly less popular than others, but enabling the app to function seamlessly with more experienced farmers would encourage more users to join

We wish to look into future climate projections and models and incorporate them into Gaia’s advice that it gives to users based on what crops they should go (for instance, if LA is projected to have a harsh winter, it may be better to grow crops more suitable for the indoors)

More data means better recommendations, so we want to expand on the metadata that we examine and make use of (there is a lot out there) to make better recommendations to users

Finally, we plan to create an API that allows users to upload pictures of their own gardens or greenhouses and generate cost-efficient setups tailored to each user based on their budget and location