Hard at Work!







Currently hard at work, planning to replicate the breadboard setup for Arduino and peripheral connections (i.e. ultrasonic, pH, conductivity sensors), as well as the spectroscopy bulb connections, on a protoboard.

Successfully programmed all the peripherals, and obtaining accurate values for all the devices. A few days left until our demo!!

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