10 Bold Predictions For Precision Agriculture

Corn Field SunsetThe future of technology in agriculture has never looked brighter, or more confusing than it does in 2016. We at PrecisionAg magazine have been at this for almost 20 years now, and we’ve seen the booms and busts, the game-changing technologies, the wild successes and the monumental collapses.

The only certain is uncertainty, and the only predictable trend is unpredictability, to quote virtually every futurist’s hedge on his or her road to making bold predictions. Knowing all we know, we shouldn’t dare to make any bold predictions about the future. Except for the fact that we can’t help ourselves.

So, let’s go ahead and indulge. Over the next 10 pages are our best shot at 10 “BOLD” predictions for the precision agriculture market over the next 24 months.

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13 comments on “10 Bold Predictions For Precision Agriculture

  1. Many think drones will be the future, but they are not the answer. Best management practices will get you through the rough times. good eye contact with the crop by actual walking is still the best when it comes to crop management.

  2. Self-calibrating sensors on field equipment is indeed a challenge that needs to be addressed. Without consistent data at the input, trying to do any automated (or manual) analytics downstream is fruitless (pun intended, as poor as it is).

      1. I think the high resolution, variable rate (VR) delivery implements (seeding, inputs, pest/herbicides) are only as effective as the data feeding the VR prescriptions. The data that can drive good VR prescriptions include prior yields, soil moisture, soil type, weather (historical, current, forecasted), imagery (whatever sources) etc. The quality and consistency of the sensed sources has to be high before any prescriptions can be derived. Then there is a reasonable chance of optimizing yields/profits/revenue while even being somewhat gentle on the environment. THEN, analytics can be created to do some interesting things and the challenge shifts to automating and presenting recommendations in a simple, non taxing way.

    1. Honestly, I cannot agree more on your points which exactly explained why only guidance products are popular in China. I was thinking the data captured by the tech devices are precisely, consistently serving you many years ago.
      The resolution of drone now is high up to sub-inch GSD(ground sample distance). The images are so inconsistent?
      Maybe the precise data is like a secret that is veiled by now☺

  3. 1. Business model by service provider (yes a problem for now) (2) cost to maintain is very low other than if you crash (3) interpretation of data – Software as a solution ? 4. Cost of camera and processing are barriers (4) You will need to plug the data into analytical software 5. liability will come down as the ag “uav” market grows 6. you forgot to mention vc money coming into this .

  4. Precision agriculture can be efficient if multiple data are correctly collected and integrated. No drone company is actually able to do so as far as I know.
    Imagery is required, only drones can give the best resolution (ground and time) and provide several vegetation indices. Integration of imagery data and soil data (chemicals not physical) is a first step. Field observations (ground truthing) are necessary too … then you can see that no software can do the prescription job.
    Precision agriculture actual inefficiency originates in the fact that marketers want a simple solution for a quick sale.
    The precision agriculture consultant can be efficient if he asks the good questions, collects the good data and takes the best possible decision, not if he collects basic NDVI and uses it simply for fertiliser prescriptions. Cheap and simple solutions just do not work.

  5. I think you indulged well! Over the past 2 decades we have continued to find more ways to gather more data to provide a greater magnitude of information to digest and hopefully finding our limiting factor(s). The problem is we really haven’t found a good ‘digester’ of all this information (overload). Weather data, both local and regional, has been around a long time with many models being built, but this has yet to become a part of our daily lives to solve questions. Yield data has been collected to the nth degree, yet quick and dirty processing to provide some sort of answer still seems to elude us to some extent. The UAV hype as we waited to hear from the FAA seems to have faded as satellites now provide daily returns. Change detection without having to task satellites to gather a single image per month is a thing of the past. The resolution of these satellites are ‘good enough’ to accomplish PAg tasks even though some think we need to be at the UAV resolution. Farms flooded with wireless technology has it’s pockets of success, but still doesn’t seem to be the norm so we can connect everything without a dozen repeaters to get data back to where it needs to be. Enough of the doom and gloom. PAg over the past 20 years has taken on new technology and made great strides in improving yield and quality. We’ve done it one technology at a time. Find the new technology – consume, digest, and regurgitate all the information to provide solutions to the never ending question of “What are my limiting factors?”. We just need to start combining all the technologies to continue to find the best solution(s)!

    1. Well Bob, you write “We just need to start combining all the technologies to continue to find the best solution(s)!”
      What if you do not have relevant data ? You can combine whatever you have and find no solution at all ! This is where we actually stand. And this is the reason why we do not get good enough results in PAg.
      The most important data has to be collected on the plant because it is the plant that gives the yield/quality.
      Then you have to know how plants work to find out which data to collect (vegetation indices) … and then integrate/combine with other data (soil, weather …). It will work if done correctly, not “quick and dirty processing” as you mention.

      1. Noel – collecting irrelevant data won’t ever help us find a solution. Knowing the tractor’s air filter is getting clogged doesn’t help me grow a better crop. I agree with that, but collecting and combining data before the plant is in the ground is a good place to start. I can definitely come up with relevant information to assist the seed placed into the ground that gives it a better opportunity. Once the plant is established it obviously becomes the best indicator of things going right/wrong. So I agree with you, but would discern that there is plenty we can do long before the seed gets put into the ground.

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