I researched and finalized the components required for the system, including all the sensors and the camera. I ensured that these components met the project’s durability and cost requirements and considered the pin requirements for connection to the Raspberry Pi 5.
I also worked on the design presentation, where I was responsible for updating the project schedule. Additionally, I researched the quantitative design requirements and linked them to the use case requirements wherever applicable.
I began researching potential plant health detection model datasets, including sensor and image datasets. While I found many datasets related to crop diseases and general health classification, I encountered a lack of suitable ones focused on household plants, specifically addressing conditions like over/under-watering, nutrient deficiencies, or excessive heat. Most datasets are centered on crop diseases or pest detection, which may not fully align with our needs.
I researched models that could be used for training the machine learning model, including pre-trained CNN models such as ResNet and MobileNet, as well as MLP models. I identified possible relevant works.
My progress is on schedule. I have completed the component research task and I am on track with my responsibilities for dataset research.
Next Week’s Deliverables:
- Finalize dataset and model selection. If necessary, I will explore the option of training from my own dataset, though I am unsure if that is feasible.
- Begin working on leaf detection using OpenCV.
- Work on my assigned sections for the design report.