Summary: We recently did an interesting test where we used NDVI imagery from one of our drones to detect improvements in plant health over a 10 day period.We found that the NDVI was sensitive enough to detect these improvements once the nutrition had been applied.
In September 2014 we launched our agricultural drone (www.agridrone.co) at the Agri Mega trade fair in Bredasdorp, South Africa. We are a small startup using PixHawk-based drones to map farms to provide timeous, high resolution RGB and NDVI imagery to farmers, agronomists and soil scientists.
Based on testing work we did the previous season, we were approached by an organic wine farmer and an organic plant nutrition developer to conduct a test of an organic plant nutrition product. The company, Agro-Organics from Somerset West, South Africa, has been working with organic wine farmer Dr Edmund Oettle, of Uplands Organic Estate. They wanted to see if the expected improvement in the vineyard could be detected by our NDVI.
We therefore planned 6 flights over a 10 day period. The plant nutrition was applied at the end of Day 1, after the first mapping flights were done. We then intended to fly on 5 follow up days and compare imagery. In the event, a mistake I made in planning the flights meant that the first day's flight was not usable for comparison, but we were still able to compare the subsequent days.
By flying the identical mission at the same time of day we managed to get comparable images. We were fortunate to get clear conditions on every day, but on some days we had quite strong winds to contend with.
Several images are attached - an RGB mosaic of the vineyard block, showing the 4 rows that were observed. A second image compares the NDVI of the four rows, clearly showing improvement in the treated rows relative to the untreated.
We also took chlorophyll content readings on a sample of the vineyard over the duration of the test, which confirmed the NDVI result.
Usually NDVI is seen as having value in early detection of stress, but I think our project shows that it can add value in identifying improvements too, within limits.
For those seeking more information on our drone, we provide details on our website.
The Cokin A003
Our policy has always been results-based. Not taking anything away from other folks with more extensive methods, but if our work can yield credible, verifiable results, we prefer to keep it simple. Flying at 50m there is no atmospheric light disturbance to speak of, and in the location we were flying at has consistently clear conditions at that time of the year. These are winter rainful areas and grapes are a summer crop, so clear conditions are pretty much guaranteed the season round.
Sure Dave, so long as you credit us in some way, I would be fine with it.
Hi- Thanks for your great article. Would you be able to provide the raw 4-band stack image so I can try my own VNIR indices on it? This is for a class. Thanks
Hi Alexander, thanks for the feedback. I generally don't make 4 band composites but I have attached the NRGs for the relevant areas of the images. Hope they can be of use.
BTW this is a 50% resize using nearest neighbour, whereas the NDVIs above were created on the original sized NRGs and then resized for this article. So some minor variation is possible.
Really, thanks a lot for posting this image set for me. It will be of great use in my class- I really appreciate it!