Terra data contains about 300,000 sorghum images of size (3000x2000). Taken in 57 days from April to June.
I group 3 days into a class. Here is some sample from each classes:
we want to train a network to predict the growing stage (i.e. date) of the plant based on the structure of the leaves. Therefore we need to crop the image to fit the input size of the network.
I used two ways to crop the image:
- use a fixed size bounding box to crop out the part of the image that most likely to be a plant. Here is some samples:
This method will gives you images with the same resolution, but ignore the global structure of the large plant that we may interested in. (such as flower)
2. the bounding box is size of a whole plant, then rescale the cropped image into fixed size:
This method allows network to cheat：predict the date based on resolution. Instead of the structure of the plant.
Another issue is about noise: both method will gives you images like these:
I don't know how frequently will these noise appear in the whole dataset and whether it is necessary to improve the pre-process method to get rid of these.
The next step will be improving these methods and train a network following one of them.