This week we started preparing for the upcoming demo by integrating various parts of the project. This included setting up the greenhouse with various hardware components in there, such as the RPi, camera, water pump, and plants. I looked into the requirements for increasing the privacy of the live streaming, and we plant to mitigate this in two ways, firstly by using an opaque screen as the backdrop to avoid capturing objects in the background, and also by limiting access to the live stream to authorized users only via OAuthentication (although this will be implemented later). I also developed the multi-classification ML model for plant health classification. I began working on setting up the LEDs for the greenhouse but ran into some issues regarding the wiring of the LEDs and how they can’t be controlled through the relay alone. To mitigate that, I have decided to use back up LEDs that I happen to have. Similarly, while setting up the misters, I realized that it may not be compatible with our RPi setup, and so I have decided to leave that until later. Since we now had the plants in the greenhouse, I tested the API identification with the plant, and it worked. I also tested that the ML health classification can capture an image and process it once a day and on command from the webapp. Since the sensors have not all been set up, I haven’t been able to collect sensor data, so I have decided to just begin collecting image data. When testing the ML model (only trained on online data), I found that the results were highly inaccurate, so we must collect a significant amount of data for training and testing purposes.
I am slightly behind schedule due to facing issues with the LED setup and lack of sensor/image data collection, however, I plan to prioritize image collection over the next 2 weeks to build a good enough dataset.
Next Week’s Deliverables:
- LED setup
- Begin image data collection
- Figure out best way to set up the mister
- Buy more plants for testing