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FarmCare Studio Segmentation feature
There are many healthcare issues in the farm ecosystem as people in rural areas are not much aware of their farm's health.
What it does
It helps in monitoring the healthcare of the farm ecosystem.
How we built it
We have done the backend on the Django Framework. Using RestAPIs for the connection. In machine learning techniques we have used Deep Learning, and several classifiers and regression models like Gradient Boosting, Random Forest, Decision Trees. For masking the image we are using Otsu segmentation technique. Using advanced CSS to make it fully responsible and compatible with most of the devices. We have used NLP for building real-time recommendation system.
Challenges we ran into
Getting a better accuracy for the machine learning models was a great challenge but we succeded in getting good accuracy through proper segmentation techniques.
Accomplishments that we're proud of
We have a full working project which has both frontend and backend connecting along with machine learning models. This is fully responsive so one will not get any struggle in running on different devices. This website has multi-language support so that rural people of different region and languages can use the application.
What we learned
We learn many new models and different ways to implement functionality and image segmentation. We also learn how to connect machine learning models with a web application which is of greate use.
What's next for FarmCare
We will connect IOT devices to get the real time data.