Question: AstroPlant RPi sensory system

I haven't used the Pi NoIR cam and have not seen your photos, so I can only respond from general principles.

1. Camera sensors are not as sensitive to near IR as they are to visible light. So even though the sensor might be sensitive to wavelengths between 700nm and 1000nm, They capture less of that range than they do in the range of any single color (R,G,B). So the ratio of VIS:NIR (e.g., Red:NIR) you can derive from a modified consumer type camera will not be similar to the actual ratio in the light being reflected from plant leaves. This ratio might even be reversed.
2. A modified camera (IR cut filter replaced with color bandpass filter) cannot produce both a pure visible light channel and a pure NIR channel. A red filter can produce a fairly pure NIR signal in the blue channel but the red channel will have red and NIR mixed in a mostly unknown proportion. A blue filter will produce a blue channel with blue and NIR mixed and a red channel with red and NIR mixed. With the proper filter combined with the proper sensor, the band of interest will dominate in the mixed channels and the hack will work. Otherwise, not so much.
3. These weaknesses can be compensated for by altering the values returned by the camera for the channels of interest. This is called either calibration or fudging depending on whether the alteration is based on knowledge of the system or an attempt to make the results look right.
4. With two cameras and the proper filters, pure NIR and VIS channels can be captured, but the issue of low sensitivity to NIR remains.
5. If you know (quantitatively) the relative sensitivity of NIR and VIS in your cameras, you can correct for this issue.

Chris

Using two cameras solves one of the two problems I described (mixed NIR and VIS). That problem can also be solved by measuring or making assumptions about the proportion of NIR and VIS in the mixed channel(s). This requires some data about the sensitivity of individual channels to particular wavelength bands. Some cameras have been characterized in this way. The sensor in the RPi camera has been characterized in the visible part of the spectrum, but I have not seen results for the NIR portion. A haphazard subset of consumer cameras has been characterized in both the VIS and NIR. You could apply those results to other cameras and this might get you as close as you need to be.

The other problem, that cameras are not as sensitive to NIR as to VIS, can be solved using the same data that solves the first problem. You need to know how much to inflate the NIR values to compensate for the sensor's lower sensitivity in that range.

Ned Horning has been working on a workaround for both of these problems that involves placing targets of known spectral reflectance in the bands of interest in each photo. You then figure out how much the brightness of pixels in the targets differs from what they should be, and use that information to adjust all the other pixels in the photo. This is more straightforward if the photos are captured in raw format so you avoid the heavy processing (gamma correction, color balance, etc) that happens when the camera makes jpegs. I think this method can be derailed by the issue of mixed NIR and VIS in each channel because different parts of the photographed scene can have different proportions of NIR and VIS, so you never really know what that proportion is for any pixel. But maybe it works when applied only to the foliage parts of an image where the NIR:VIS ratio is more predictable.