Methods and Software used for stand counts


I was wondering if anyone could share their experiences with estimating populations on photos they've taken with UAVs.  Software or particular methods that you have found to work.  I currently use a Y6 tri-copter from Event 38 with a GoPro Camera as well as a Canon SX 260 converted to near-infrared.  I've used Agisoft Photoscan with great success in photo-stitching and dronemapper. I've dabbled with Fiji/image j, and Microsoft ICE but I've found they seem to have more of a learning curve for me.

Any ideas if population estimates work well in any of these programs or in others that I have not mentioned?



You need to be a member of diydrones to add comments!

Join diydrones

Email me when people reply –


  • Hi Taylor, I've sent you a friend request so we can connect and discuss where you are up to with your plant count technology. I am involved in some research trials in Australia that could really benefit from this technology and I may be able to provide some crop imaging you've not test previously if that interests you.

    Tony Gilbert, Queensland Drones

    Taylor Glenn said:

    Hi Nasruddin,

    I can't direct message you since we're not friends yet on the site. So I'm posting a reply here before I forget.

     I'd be happy to take a look at your images. You can email them to me if they are small enough, or send me a link from dropbox, google drive, etc. if that is more convenient. Email: tcg [at]



  • Hi guys,

    I just came across this about performing stand counts / crop counts, using drones. There are lots of technological developments in precision ag, but sometimes I wonder what the practical use is.

    Any chance you can tell me why this interests you? What crops are you growing? What decisions that can be made, based on stand counts, and what are the benefits? 



  • A new startup company from MIT, Raptor Maps, is working on bringing a cloud-based solution for this (image stitching and stand counts, etc). 

    Check them out at and contact them. 

    • Ok My friend, I visited your new company's website but I could not find any information about software or service cloud-based.

  • Hi Morgan. I have some internal tools I have made for counting plants from high resolution imagery.

    If you can resolve the individual plants in the imagery, then we can do a fairly accurate whole population count, with the main sources of error being any completely overlapped plants and errors from the mosaic. If the individual plants are not resolvable, then we may be able to do some straightforward math based on density and vegetation area in order to get an estimate.

    If you have some imagery that you'd like to share, I'd be happy to run my software against it and see what kind of results we can get.

    best regards


    • Hey Taylor,

      Fantastic software you have developed. Can this be applied to NIR images as well? I have some NIR images you can test it on if you are interested?


    • Hi Martin,

      Thanks! Yes, it should work just fine with NIR images as well. 

      I would love to see your images and test against them. You can email me at tcg [at],

      or send me a PM through the DIYDrones site and we can arrange another method of transferring them.



    • Hi Taylor,

      I should be able to find some imagery to take a look at.  What file format do you prefer? And does the flight altitude matter, the crop, or whether it is a stitched mosaic or an individual shot?

    • Cool. Sounds great!

      Any format is fine, though GeoTiff is preferable if you have the georeferencing information.

      Uncompresssed (raw/bitmaps) are better too, even at a 2cm/pixel GSD the compression will often start to blur the fine details that we might want to delineate the individual plants.

      For altitude, the lower the better, but its all tradeoffs really. The lower you are the more likely you can resolve the individual plants, though the data size starts getting unwieldy. I have written my software to handle multi-gigabyte mosaics however, so its not too big an issue. It just takes longer to run and longer to transmit the data.

      What kind of crops do you have? My prototype was build around some imagery of potato fields shortly after the plants emerged. The post-emergence time is good because the plants have generally not completely overlapped and formed an indistinguishable canopy. A dense mature corn or soy field may be a bit more challenging (though maybe not impossible).

      For starters, it would be easiest however to just run against an individual shot. That will be sufficient to see what we are up against and get an idea of how well the algorithm works for that application.

      If you want to upload some images to Dropbox or Google Drive and send me a link via direct message that would be fine. Or email me tcg [at]

      Looking forward to it. Thanks


    • Hi Morgan,

      I was able to get some good results from one of your corn images.


      In this one, if you zoom in, you can make out the individual corn plants.


      With this level of detail, we can do a pretty good job at automatically identifying and counting all of the plants. It is a challenging problem, however, and if the plants have formed more of a canopy it will start being much more difficult to tell them apart. As it is there are still a few tricky cases that are a matter of interpretation. I estimate we are correct to within a few percent, but this will require a controlled collection with ground truth to verify. Within this small sub-image it identifies 83 plants, my hand count from the same area was 85.


      The full image from the GoPro is actually a bit too wide-angle to be useable over the whole image, the perspective changes from overhead to side-looking as you get to the edges of the image.

      I would recommend you set the GoPro to the narrowest Field of View setting for your future collections. This will help with the mosaic as well because it is easier to match the images if they are all from the same perspective (ie looking more-or-less straight down). Also be sure to do everything you can to reduce vibrations and keep the lens clean, there are some other smearing artifacts that are affecting the image quality as well, and every little bit of detail helps in the tough cases like this. 

      Running this process on a larger section of the image, where the perspective is still mostly straight down, it identifies 1,132 plants. Of course I didn't try a hand count to verify this =)


      I can identify a few errors where it adds a plant where I would not have, or misses one I would have counted. In all however, I think the error rate is low and relatively unbiased, so the number is probably close. (If anyone wants to work together for a controlled experiment, let me know, I'm happy to talk!)

      So, what's next? I can run these algorithms against a full mosaic if you have one and get a population count for an entire field (so long as it has the same level of detail).

      What do you all think?



This reply was deleted.