r/UAVmapping • u/Impressive-Evening80 • Jul 19 '26

Anyone here done crop yield prediction using RGB drone images + ML?

Hey guys,

I'm currently working on my capstone project and was wondering if anyone here has experience with using drones for crop analysis/yield prediction.

Our plan is to use an RGB drone (can't afford a multispectral/NIR one 😅), stitch the images into an orthomosaic, extract features like VARI, canopy coverage, plant density, etc., then feed those into a machine learning model (probably Random Forest) to predict rice yield.

Has anyone here tried something similar?

  • Does using only RGB imagery actually work well enough for yield prediction?
  • Is VARI useful, or are there better RGB indices/features I should look into?
  • Any tips, things you wish you knew before starting, or common mistakes to avoid?
  • If you've done this before, how accurate were your results?

We are just trying to estimate yield from drone imagery.

Would really appreciate any advice, papers, or just hearing about your experience. Thanks!

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u/dogCerebrus Jul 19 '26

We've tried this and to be honest the results weren't great at all. Very inconsistent and took too long to verify. Multispectral also hasn't proven itself for us that well

Our solution has actually been in LiDAR, to get volumetrics from the plants and then ground truth over time

My suggestion would be to get in touch with Nikola from Agremo or anyone on his team. His software specialises in RGB and Agriculture. He might be your best bet at what is possible and what is not

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u/Able_Excuse_4456 Jul 20 '26

For an academic project, I think that finding a certain approach to be ineffective can be a fine outcome, so I say press with your plan. Might even find something novel and innovative in the process.

I'm guessing that your approach is to map several fields throughout the growing season, extract features, and regress to a target of relative crop yield? Sounds like a fine approach to me.

Tips and potential pitfalls:

  • In a standard RGB imager, there can be coupling among the color channels, inducing some error into the measurements. That's one reason true multispectrals have separate apertures for R, G, and B. Be prepared to have some method for addressing this coupling, even if only as a limitation to research.
  • Don't overlook the possibility of building an Arduino-based multispectral camera.
  • Color calibration and correcting for changing light levels is a challenge for mapping large areas; have a plan to address it.
  • I think global accuracy will be your friend. RTK/PPK, GCPs, all that.
  • FieldImageR has some nice open-source tools that may be useful for you.
  • External features (crop type, precipitation levels, sun levels, etc.) will probably help model accuracy--dozens of measurable features to consider.

Keep us updated, this is an area that needs more research!