Software Design - Raspberry Pi Communication to the Cloud

This post outlines the three routine steps for RPi communication to the Firestore database.

  1. Sample State
    • This routine step waits for the Arduino to inform the RPi about the latest state of all the sensors (contained within the data packet sent by the Arduino to the RPi). Information about the command package and the data package can be read in an earlier post.
  2. Check Garbage State
    • This state checks the garbage level within the Lilypod, above the filtering mesh, with the use of the ultrasonic sensor. If the Arduino registers that the ultrasonic sensor is reading a value above a specified voltage threshold (to be determined based on testing), the system will completely shut down, and await a farmer to conduct a manual reset of the pod. Before the manual reset of the pod can occur, the filter must be cleared out.
  3.  Filter State
    • One of the states is a filter state, where all the doors (trap and garage) are open, filtering water through without conducting any spectroscopy, pH sensor or ultrasonic sensor checks. This is a state in which all actuating components are checked for appropriate functionality. This is usually conducted at the start of every treatment cycle, ideally after a manual reset has occurred.
Based on these three routine steps, data is logged within the Firestore database and displayed by on the visual dashboard through the client.

Comments

Post a Comment

Popular posts from this blog

Mechanical Design - Watertight Locking Mechanism

Image
To solve the issue of protecting the electronics from water damage from the external water body, several locking mechanisms were explored, connecting the middle interface (i.e. spectroscopy chamber and internal water flow), the bottom interface (pump and mesh) and the top interface (pad and flotation devices). This was explored in a manner which would ensure no water would flow between the connecting parts. Below is a rendering of the chosen locking mechanism: The above part was meant to serve as a prototype, where a latch from the above part would lock under a solid extrusion in the bottom part, similar to a tupperware container. Based on this prototype, the locking mechanism would be placed at three calculated points around the circumference of the entire Lilypod, for each interface between connecting parts (i.e. three for each interface). This prototype was sent for 3D printing to be tested. Check back on the outcome of this test!
Read more

Final Post (Poster, Video)

Image
With the ongoing COVID-19 situation and the symposium being cancelled, our team was unfortunately unable to demo our project to the public last Friday. Our team put together a video of all the functionalities of the final product, which you can view below. As well, we created a poster outlining all the important pieces of information about this product. Hope you all enjoy!
Read more

Software Development - Making Lilypod Intelligent – Part 3 (The Implementation)

Image
To implement the neural network, a Tensorflow backend with a Keras wrapper was used. The following is a visual representation of the network: It was designed to not be too deep since we do not have access to any pretrained layers that would be useful, nor do we have enough data to train the large number of parameters that a deeper network would have.  In addition to the model itself, an interface was built to allow hydroponic farmers to train the network with new data as well as to obtain a crop quality prediction when desired in an easy to use manner. To do this, a desktop app was built using Pygame and Matplotlib. The app displays a dashboard, showing graphs of the latest sensor readings of the water. Located beside the graph is an interface with two text fields and two buttons, each corresponding to a different text field. One text field allows farmers to input a crop score based on their own expertise. The button corresponding to that text field, when clicked, runs a
Read more