Additional Measurements: Soil moisture, temperature, air temperature, relative humidity, Solar radiation flux (with a pyranometer), amount of precipitation, from the RGB the calculated “amount of greenness”, percent aerial cover of plants with SamplePoint (Some kind of software, I have to look into this)

General Setup: 6.1 megapixel camera; Nikon D70 digital single lens reflex camera; camera at a height of 2m pointing at the plot with a 45 degree angle;  The camera was continuously powered and connected to a laptop that controlled it; picture every hour; Camera was set on “auto” to allow f-stop and shutter adjustments; camera’s focal length was set to 0.67mm, this gave a set pixel resolution.

Image Processing: The object classification depends on colour thresholding.  The different thresholds were chosen “by hand”.  Naturally, HSV was chosen for the image format. The ?Matlab routine “euler” allowed them to go from pixels to discrete flower elements. It states that it used the “clean”, “majority” and “close” Matlab routines, but when I searched for these routines on my Matlab 7.9.0.529 (R2009b) I did not find them.  If they used some non-standard Matlab package, they did not mention it.  It is mentioned that confusion arises when two flowers have same colour.

Comments:

1. While the paper mentions confusion from flowers of the same colour, it does not address the case where the element that is being classified does not have “good” contrast with the background.  They DID modify the saturation threshold for one type of flower, but this does not address the issue in general.
2. The HSV values were examined “manually” to determine the threshold.  Not sure how this was done but an automated process based on minimizing the variance between “positive” and “negative” pixels would be more general.
3. It made a really cool argument about repeat photography as phenological tool: “Compared with repeated manual visual observations, this approach is inexpensive once the initial investment in camera equipment has been made.”
4. It points out that occlusion is one of the drawbacks from repeat photography.  It goes on to say that it might be best to have a combination of repeat photography and field observations.
5. They mention that the greenness index used worked well in direct sun light, but presented anomalous behaviour in shade.  The propose this to be a good research area.

This paper gives a general direction that I believe is close to where we are headed.  Furthermore, I think we have a lot to offer in terms of Classification, logging and information retrieval techniques.