forked from TinajaLabs/gateway_raspi
testing a series of scheduling algorithms for a Raspberry Pi-powered gardening system
valkyriesavage/fraiche
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
master
Could not load branches
Nothing to show
Could not load tags
Nothing to show
{{ refName }}
default
Name already in use
A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code
-
Clone
Use Git or checkout with SVN using the web URL.
Work fast with our official CLI. Learn more.
- Open with GitHub Desktop
- Download ZIP
Sign In Required
Please sign in to use Codespaces.
Launching GitHub Desktop
If nothing happens, download GitHub Desktop and try again.
Launching GitHub Desktop
If nothing happens, download GitHub Desktop and try again.
Launching Xcode
If nothing happens, download Xcode and try again.
Launching Visual Studio Code
Your codespace will open once ready.
There was a problem preparing your codespace, please try again.
This branch is 139 commits ahead, 2 commits behind TinajaLabs:master.
Latest commit
Git stats
Files
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
This is a fork from tinajalabs' gateway_raspi The system components are as follows: devices co-located with plants * solar panel, moisture sensor, XBee radio, 4 buttons, servo controlling open-ness of drip irrigation valve * every hour, wake up, take soil moisture reading, and transmit via XBee * after transmit, wait several seconds to see if water levels or thresholds need to be adjusted (as informed by Pi) * four buttons controlling threshold and water amount can wake the device for transmit Raspberry Pi * machine learning performed on moisture data from plants, determining what times and moisture levels lead to watering. this data will be used to automatically water the plants when the user desires. * webserver runs a simple page that displays the moisture level of each plant connected to the system. users can also use this page to adjust settings on when the plants should be watered, which are sent to the plant-based XBees at next communication * XBee mounted on Pi is a gateway for all XBees on plants; it receives moisture level readings (which are fed into the machine learning algorithm) twice an hour, except when devices are awoken sooner by user interaction Look at the import commands and you'll see it will require the following python modules: * pySerial * tornado
About
testing a series of scheduling algorithms for a Raspberry Pi-powered gardening system
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published
Languages
- Python 58.4%
- JavaScript 21.0%
- Objective-C 12.6%
- Shell 5.3%
- Ruby 2.7%