The problem AnnData (अन्नदाता) solves

1. Buying and Selling space: Products can be sold and bought in bulk according to their quality. This service can create a competition between bestselling quality products and can offer services for unique Agro products according to the demand client partners.
2. Compare the market prices: Person in Agro industry cannot compare the prices of product. So, they can compare the prices of products.
3. Buying of raw inputs: Fertilizers, manures used are of inferior quality. An excellent quality product (fertilizer) can be put up on the site. It can provide wide of product in Agri input sector especially fertilizers, manures, micro nutrient fertilizers, bio pesticides, Agro chemical, plant hormones and plant regulators.
4. Basic awareness: It can be served as smart agricultural platform that provided end to end integration solution increasing the yield efficiency and profitability and complete traceability for all the agricultural stake holder.
5. Constant notifications for farmers: Sending them constant emails regarding purchases, buying quotes and also their applications of various product materials. We also give the numbers of those who are interested to buy items
   6)Compare the market prices: Person in Agro industry cannot compare the prices of product. So, they can compare the prices of products.​
   7)Prediction model tells users the exact crops suitable for the given climate and soil conditions.​
   8)After prediction the Guidance model will provide the weekly information necessary to grow the crops along with the right amount of Fertilizer and Water required. This ensures that the wastage and hence pollution is reduced.​
   9)We have integrated an e-commerce store to our platform so that the users can buy crops directly from the farmers. This will also enable urban farmers to sell their surplus. All the users will also have an added benefit to buying various tools and fertilizers from the platform.

Challenges we ran into

While working on this project, we had to do a lot of field research regarding our use-case especially because it concerned farmers and we were looking to keep it simple and efficient. We also ran into the issue of modelling the schema, so as to produce a normalized and efficient database. There were certain issues with working on technologies like AngularJs and NodeJs especially with functions like aggregation, indexing and embedding of data, both between frontend and backend. All in all we as a team learnt a lot through these challenges, and are happy to have overcome them and produced an efficient product.