Our team initially consulted with Eagles For Environment (EFE), a nonprofit organization that tackles Blackland prairie restoration in North Texas. The question of planting seeds in the fall vs the spring season was one that sparked debate among restoration experts and master naturalists. Given the high costs of seeds and tremendous manpower it takes for restoration initiatives, having some methodology that provides more information on when to seed would be crucial in guiding this decision.
Our project utilizes a ground up Random Forest ML model that trains data based on past restoration site information. Additionally, we make use of Gemini's NLP capabilities to make the data acquisition process smoother and more conversational rather than relying on mundane surveys. Simply talk to a chatbot which asks you questions about your upcoming restoration site's conditions and provides you with the final suggestion on whether to plant your seeds in the spring or in the fall.
We built our product using Google's Gemini LLM which had the job of asking questions to the user and parsing their answers to gather the most important information: the various data types and quantities of a restoration site. After training our Random Forest ML model with historical restoration data, we were able to accurately run our test data. This test data primarily consisted of the prairie restoration projects conducted over 3 years by Eagles For Environment. EFE had two fall seedings and one spring seeding. Like all restoration sites, the three EFE restored had their own unique challenges and features.
Integrating all aspects of the project from the front end UI to the connecting database, to the Random Forest ML model took much much longer than anticipated. Realizing that each aspect caused other dependencies to fail when put together was incredibly confusing at times. In the end, it would have been better if we started by integrating smaller aspects together sooner instead of attempting to integrate all aspects of the product at once. Our UI design and animations strongly align to the theme of environmentalism and restoration. Our ground up random forest model is our favorite aspect of the project.
For RestoreAI, our next move relies on collecting more clean and reliable data. We could branch out to different restoration organizations to gain more data outside of the Blackland prairie. To help restorationists identify certain invasive species, we could employ computer vision to categorize pictures taken by restorationists. We could also add more outside of the chatbot and look into the restoration sites' geospacial maps to highlight areas of focus for seeding or make use of computer vision technology with satellite/drone imagery to identify and address invasive species early and effectively. The ultimate goal of RestoreAI is to move into the agricultural industry where various ML models can provide data driven insights to farmers enabling them to harvest greater yields.
ai, appwrite, gemini, javascript, ml, mysql, python, pandas, numpy, react