Inspiration
The USEPA estimates average daily wastewater flows of approximately 50 to 70 gallons per person per day being typical of residential dwellings built before 1994, (USEPA, 2002). Imagine for a second how much water it takes to water a field the size of a house let alone a farm. We know we can do better and through a custom sensor device and AI/ML analysis we have developed an intelligent way to manage agriculture.
What it does
Users get an easy to use IOS app that gives them direct access to the IOT monitoring sensors. From here a user can get an alert on the status of any sensor indicating a problem and use one of the actions such as water plant. All of a users data is combined into an easy to understand graph. Users also get to utilize a desktop dashboard that shows the sensor data in an interactive way while also showing alerts.
How we built it
This project was built using a complex series of hardware integrations, routing data through our API to process and returning easy to read graphs and tables.
Challenges we ran into
Integrating all of the components into a working mesh was difficult. We then had to develop an API to handle all of the data that gets processed by our machine learning algorithm into easy to understand graphs.
Accomplishments that we're proud of
It looks great :)
What we learned
Ardunio sensor data analysis, Mobile App Development, Converting data to graphs.
What's next for SmartSpray
Deployment.
Built With
- ai
- arduino
- c
- gcp
- grafana
- machine-learning
- moisture-sensor
- mongodb
- mysql
- node.js
- python
- raspberry-pi
- swift
- uv-sensor
- vuejs
- xcode
Log in or sign up for Devpost to join the conversation.