This week, I worked on my part of the design presentation which included defining the quantitative design requirements. I also chose and ordered the live plants for the project, which means we now have a clear direction for data collection and analysis.
For the plant health prediction ML model, I selected the datasets to be used in training, opting for images of entire plants instead of just leaves as originally planned. As many of the available datasets didn’t have all the necessary data, I chose to combine multiple datasets to include the required abiotic and biotic stress factors. Since the dataset consists of full plant images, the leaf detection step may not be necessary for now. Instead, I plan to train the ML model on these images directly and will only preprocess the images if needed, based on model performance.
I explored several potential models for plant detection and health classification. YOLOv8n/YOLOv8s stand out as suitable options for object detection. These models would allow the identification and labeling of multiple plants and their individual parts within the images, which is suitable for our 3 plant system. Additionally, I’m considering ResNet50 for better overall performance or MobileNet, which is more lightweight and better suited for deployment on an RPi 5. My plan is to train multiple models and evaluate which one performs the best.
I am on schedule.
Next Week’s Deliverables:
- I will start labeling the datasets consistently, given that the images come from multiple sources. This will ensure that the training data is standardized.
- I will also perform data augmentation to create a larger training set.
- I will also establish the initial training framework for the model.