[eobropta]

Hi James. This is Eddie, the CTO of Raptor Maps. We figured out the combination of wavelength bands we need, and combining that with image recognition we can identify particular diseases. We had to do a lot of iterations and correlations to chemical samples to build up confidence that this can actually work. We can only do a few specific conditions today in potato farming, so I can't say this works for every disease everywhere, but we'll keep getting better over time and are working quick to do so. The sensor package is optics-lab style cameras tuned for wavelengths we know have the best signal to noise ratio.

With regards to harvest monitoring, by knowing the exact yields and locations we can correlate remote sensing data to the yield. This type of information will help with management in the coming seasons. But it can also help today. For example, you want to move your lower-quality inventory more quickly so it doesn't affect the good stuff, so you want to put it by the door of the storage facility.

[James001]

Hey Eddie, thanks for getting back to me.

I was wondering when image recognition was going to play a stronger role in the use of drones for agriculture. When I was doing it, it was simply using combinations of spectral bands and not really delving into pattern recognition. Sounds like that's the right way to be headed. Obviously just at the beginnings of it and I appreciate that you know the current limitations of the technology. But still, that's impressive.

Also curious if you are using satellite imagery in any contextual way or to train your image recognition in any way? Or if you are using the lIDAR already available at all? I imagine you are collecting higher-resolution LIDAR when you fly over?

Anyway, good luck with this, sounds like you guys are doing it right.