The problem Mitti Mentor solves

1. Increment in Agricultural Yield: With the correct analysis of the soil texture, the right crop can be easily predicted thereby increasing the crop yield.
2. Efficient use of cultivable land: With our model, Farmers can get a list of crop suggestions with which they can maximize their production output in a given amount of land thereby catering to the most efficient use of their cultivable land.
3. Mitigating misguidance and biased advice: Our model would be efficient enough to provide the farmers the necessary suggestions thereby eliminating the need to hire a separate consultant, thus mitigating their biased opinions generally in favor of the company paying them commissions, rendering farmers independent.

Challenges we ran into

● Implementation of ML & Backend: Significant challenges were faced during the deployment of the model as the allocated RAM in the free version of Replit overflowed due to the tensorflow and Python packages.
● Deployment of the Model : Lack of free Space in Repelit due to the lack of funding and free subscription forced us to prototype the project in a free server which curtailed our capabilities a bit
● Scalability and competition from existing products in the market : During our surveys, we came across an already established startup ‘fasal’ which worked on similar lines which made us realize that we might lack a bit in R&D and Deployment. However, our future plans include more extensive market & field surveys to enhance the accuracy of our model and provide a better user experience by the use of location, weather and other such effective parameters.