This week, I worked on my part of the design report which included the abstract, introduction, use-case requirements, design requirements, ML-specific design trade studies, ML-specific testing and validation, schedule and task division, and the related works section.
On the technical side, I completed labeling the datasets consistently, ensuring uniformity across images from multiple sources. I also finalized the data augmentation techniques, including rotation, greyscale conversion, and flipping, and have begun implementing them. As a result, I now have a fully labeled dataset ready for model training.
Additionally, I established a plan for testing live plants. I am monitoring eight plants under different conditions to support model training and evaluation. This includes 2 plants being underwatered, 2 being overwatered, 2 with nutrient deficiencies, and 2 healthy plants.
I am slightly behind schedule as I have not yet established the initial training framework for the model due to midterm exams and other deadlines. To catch up, I will continue to work through spring break and dedicate additional hours over the next week to make progress.
Next Week’s Deliverables:
- Establish the initial training framework for the model
- Test different model architectures (ResNet18 vs MobileNet) using the available dataset
- Compare model performance on greyscale vs RGB images
- Continue monitoring live plants for testing and validation