Smart Agriculture

Inspiration

News headlines caught our teams attention and we thought that if farmers got all the information they needed from one application their productivity could be increased.

What it does

The application first registers the user by taking details such as name number and location. Then according to the location of the user the app predicts weather conditions for the next seven days. It also gives insights on the soil type of the land used fir farming by the user. Finally it suggests crops that would be he most suitable to grow given the condition of his land. The app also allows users to buy products and accessories such=h as seeds, pesticides, fertilizers etc. from the E-commerce tab.

How we built it

We first started by creating a react frontend and styling it using bootstrap(CSS and jQuery). Then we used Mongodb to store the account details of the user. Moving onto backend our team used uagents library provided by Fetch.ai to create agents which got us data on soil and weather separately.

Challenges we ran into

some challenges our team faced:

1. Learning about the working of uagents and getting familiar with it usage.
2. Executing an end to end app developed through MERN stack. learning how their components interact. 3.How to run a python script file i.e.. our weather and soil agents, in a node.js backend.

What we learned

1. How to use uagents library provided by Fetch.ai
2. how to integrate a python file in a Nde.js backend 3.How to print data fetched by the agents from backend. 4.basics of MongoDB

What's next for Smart Agriculture

The next step for smart Agriculture is to create a daily activity tab which will give the user daily targets to fulfill in his farming duties such that he gets maximum yield. this daily activity tab will comprise of todays targets, Your performance rate, and new suggestions.

Built With

• css
• express.js
• mongodb
• node.js
• python
• react