Disease in Vineyard: best indice?

I’ve surveyed a particular vineyard plot in which I know there is a strip of yellowing vine, due to disease. I have processed the plot through various indices including NDVI, GCI, ReCI and NDRE but the location of the stressed canopy is not really showing. Other than already known lower canopy cover (and hence soil showing) I can’t see anything specific that I would call out, if we didn’t already know there was a problem.

EDIT: the GRAPES are showing the yellowing, which is a disease indicator, believed to be Powdery Mildew. I would expect to pick this up on MS.

Is there a better / recommended index that I could use?

In the screen capture below, the known issue is roughly in the box. Note that the red colour in the map index is soil. I’m looking at the vine (green) to try and detect a variation in the reported colour and index value.
It would be really useful to be able to get the index value for a map point selected by the mouse…

Any advice would be hugely welcome!

How does the RGB imagery look like at this place ?

Good question, I’ll run the RGB now. Normally I only run TIFF for multispectral.

It’s difficult to see it clearly but you can start to see it here.

Zoomed out, RGB

Adding to this: I found a ‘Powdery Mildew Index’ with the following formula:

PMI = (R520-R584)/(R520+R584)-R724

So think for Pix4D that would translate roughly as (Green- Orange?!)/(Green+Orange?!)-Red

Obviously ‘orange’ is an issue…any ideas folks?

Those are the wavelengths for the m3m bands:

So for R520 you could try with the Green band, but its still a couple of nm off.

In general for this kind of dataset, you biggest enemy is the area between the rows, because it will throw of the complete index visualization. You can try with MagicTool to detect it, and then Trim it away so that only the data from the actual rows will be left for analysis. The next version of PIX4Dfields will have tools to help with that.

1 Like

Perfect, thank you!

I’ve got around that by creating a boundary which has sub-boundaries per row. It’s time consuming but does remove the ‘in between’ areas, focussing the index on just the vine.

I would suggest to take sample on the field where the area got stressed due to disease. Then you overlay with indices (NDVI, NDVIRE, LCI, etc) and play with threshold value indices (classification). Hope you find correlation with that.

1 Like