I'm studying plants - specifically how the color of their leaves change throughout a day. I have a camera pointing at a leaf which takes a picture every 47 minutes and calculates the average green color.
The result is a timeseries which repeats itself every 30ish samples (plus some noise). It's not particularly well-controlled and the lighting levels change sometimes if e.g. someone turns a nearby light on, an insect happens to be on the leaf when the picture is taken, etc.
My question: How should I go about getting the average shape (exact scale and mean value are unimportant) of this variation over a day?
I have a rough 3 step plan in mind, though I have uncertainty on all 3 steps:
- Discard data from periods where the signal is obviously useless (some days it's beautifully clear, others not so much e.g. someone turns a light on and the level shifts destroying the shape information).
- Progressively average together all the sections of interest into a single representative one (Just a straight up average? Can I use a weighting scheme based on a SNR or quality metric?)
- Decide when I have enough data that I can confidently say I have recovered the shape to a given degree of certainty (using bayes perhaps?)
I am definitely open to getting deeper into some DSP or stats theory to get me through this - if anyone has any pointers I would really appreciate it!
I would also like to know if there is a name for this type of problem (catching enough identical observations until you can faithfully describe the event) I'm sure there is one!