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Into the Cloud — Exploring MicaSense Atlas with senseFly’s Nathan Stein


Share | 05/04/2017

senseFly recently began offering MicaSense’s Atlas cloud-based processing and analytics solution as an option alongside its Parrot Sequoia-based agricultural drones. To explore why the company chose Atlas and to explore what this cutting-edge online tool can offer, Waypoint caught up with senseFly’s ag solutions manager, Nathan Stein. Here’s what we learned…

Hi Nathan. To start, could you briefly explain who you are and a little about your professional background?

Sure thing. I have a degree in civil engineering from Iowa State University and have spent more than ten years gaining surveying, GIS and precision agriculture experience. As senseFly’s ag solutions manager, it’s my goal to use this collective knowledge to expand and enhance modern farming practices. At the same time, I’m a grain farmer, so I directly use and test a range of ag drone applications on our family farm in Iowa.

What does the senseFly/MicaSense partnership mean for professionals working in the agriculture industry?

This partnership means that any current or future senseFly operators can use MicaSense Atlas with senseFly’s eBee SQ, or any senseFly drone equipped with a Sequoia sensor, creating an automated and easy-to-use crop monitoring system. It’s really a great answer for users who are looking for an end-to-end ag solution. A solution that can collect, store and process data, and then allow users to analyse and share that data. I think many of the farmers, agronomists, crop consultants and service providers I’ve met over the past few years will really benefit from this.

It’s a great answer … an end-to-end ag solution that can collect, store and process data, and then allow users to analyse and share that data

You’ve used MicaSense Atlas a lot. What would you say are the top three benefits of using MicaSense Atlas with an eBee SQ?

The eBee SQ, with its eMotion Ag flight planning software, is already a really efficient data collection system. So combining that with Atlas’ efficiency and easy data management creates an even more robust solution. Both the eBee SQ and Atlas were made for users who need something that is easy-to-use yet provides useful results.

For example, after the drone’s flight, the user just uploads all their images to the Atlas cloud. The system does the rest. All the data is organized and sorted by a user’s farm/field boundaries and date of flight. With local PC-based processing systems, sometimes it can be difficult to keep track of all data being gathered during the course of a season, especially if you have multiple pilots. With Atlas, organising such fleet data is simple.

The second main benefit relates to data management. The eBee SQ’s multispectral data, from its Sequoia camera, produces calibrated datasets. This, in turn, means a user can compare imagery from one day to the next, using a handful of different indices computed for each flight. But that, of course, means finding and loading up that data. With Atlas, it’s easy — you can easily upload datasets up in just a few clicks, so you’re not wasting time looking for data in different folders on your computer or digging around an external drive. Having your processed data and ready on demand when you have a quick question is really powerful.

Because the platform is cloud-based, you can access your data anywhere there’s a connection

Thirdly, Atlas is mobile. Because the platform is cloud-based, you can access your data anywhere there’s a connection. That means it’s available in a field, in a tractor, from your office or while you’re checking the cattle. Farm life is busy and we don’t have that many chances to sit down at a computer, but our smartphones are always with us. Because Atlas has built-in field annotations and advanced visualisation tools, you can customise a map on the go and share it with your agronomist, scout, advisor or retailer. This means action can be taken and critical decisions can be made, quickly.

We understand you used Atlas extensively last growing season. Could you give us some examples of how exactly you used the platform, so that readers can start to understand and imagine how they themselves might use it?

Absolutely. Let’s look at four distinct cases from last crop year when I was testing.

1. Monitoring crops for damage

Being as busy as I am, I don’t always get a chance to walk all my acres to scout for issues. With a quick flight I can see it all. Especially in Atlas, I can practically watch a field grow. That said, sometimes things do change quickly and surprise you. Take a look at this map:

Something changed suddenly on this field. As you can see, the corn rows going from north to south are somewhat scrambled and not straight and neat as they should be. This is not normal.

Once I went to the field and navigated with my smartphone to these areas, the issue was immediately obvious: several plants had blown over and broken off. We refer to this issue as “green snap”. It occurs when early season corn, which is growing rapidly and tends to be brittle, is hit by a high wind event. It depends on genetics but no corn plant is perfectly immune. In this case, we lost quite a few plants and many were lodged over pretty severely.

greensnap_green_snap An example of ‘green snap’ (source: Wyffels Hybrids).

If you reference the map again, you can zoom in and notice the row shifts occur in lines traveling mostly in the northeast direction. You can even see deep shadows where the plants have been knocked down. Unfortunately, this issue cost about 20-30 bushel per acre.

We could easily use the Atlas tools to calculate the area of loss

Assuming we had insurance for green snap, we could easily use the Atlas tools to calculate the area of loss. Then with our crop adjuster, we could use appropriate methods to estimate the extent of the damage. This would provide a pretty accurate and fair method of adjustment and insurance payout.

2. Detecting fertiliser deficiency

In another situation, a mysterious dark pass helped me understand the impact of nitrogen and become a better equipment operator at the same time. Nitrogen is a critical nutrient required for chlorophyll production and plant growth. So what happens when you don’t put any on? We found out accidentally — at least for one area — when I noticed a strong angled pass emerge from my NDRE index and chlorophyll map in the center of the east 80 acres of this field.

After noticing the width of the pass and the angle of travel, I realised what had happened — we ran the anhydrous tank too low and didn’t apply any nitrogen. How could I confirm this? I put boots on the ground and navigated to these spots to take tests. The results from the tissue test from the low area indicated a nitrogen level of 2.16%, versus other areas of higher NDRE index and chlorophyll map scores which had 2.91% and 3.00% nitrogen.


My best guess (since it wasn’t a full pass across the whole field) is I must have switched tanks and started re-applying, but unfortunately, I didn’t reapply far enough back. This wasn’t a huge mistake but it was useful in that it provided evidence of what happens when you have application issues that go undetected.

So, what was the difference it made to our yield?


In the picture above, you have ears just before harvest, located in the area that had a low NDRE index and chlorophyll map score. In the picture below, you can see ears that were just five rows away but which received sufficient nitrogen. Notice the quality of plant tissue, not just the ear size. Using my yield monitor I found that the yield difference was approximately 100 bushels per acre, for the third of an acre impacted.


This shows how significant nitrogen is, which thankfully can be detected with the eBee SQ and Atlas maps quickly and accurately. It’s easy to see if the area or issue was large enough. I perhaps could have done targeted applications of nitrogen to attempt a “rescue” of the crop.

3. Monitoring crop dry down — preharvest

Another application situation where I found Atlas useful was during senescence; the time when the crops die and dry down. This is handy for harvesting if you know where to take a moisture sample before you send in the combine crew.

Looking above at the NDRE index at the end of August, you can see the vigor leave the crop quite rapidly per hybrid. The west side of the field is a shorter season corn (102 day maturity) vs. the east side which is a longer season corn (104 day maturity). Using this map, you can track early hybrids and late hybrids as each plant shuts down across the field. It’s actually quite a remarkable thing to see in real time.

If you make a simple comparison between the Atlas data and my yield moisture map, you can see the similarities

The biggest difference was in the replant areas, which have incredibly high moisture. This shouldn’t be a surprise as this corn was planted much later and has not had as long to mature as the rest of the crop. Without a doubt, there is a strong correlation between these two factors because if you make a simple comparison between the Atlas data above and my yield moisture map below you can see the similarities, especially the replants.yield_moisture_map4. Evaluating and measuring replanted acres

The last example I want to show is how Atlas can be used in evaluating decisions after a quick flight with the eBee SQ. In so many farming situations we make decisions that are hard to verify or understand. Replants, for example, are always a bit of a gamble. They might happen after a big rain event, or in winter wheat after a cold winter. It’s a necessary part of the cycle and many people want to know, should I replant? Did it pay?

While I don’t have the dataset from the early part of the season, I do have some ground pictures. The image below is what it looked like before replanting. Obviously, very few plants emerged and there was in general a very poor stand. It required a replant, but did it pay?


Looking at the later picture from July, you can see just how well those replants are doing. You can see clearly the bright square areas in this NIR map. These are the locations of each area where we dropped the planter and re-seeded. In fact, we may not have been liberal enough with it and in some areas should have replanted more.

In any case, it’s obvious replanted areas survived well and had strong stands. We can even total all the areas with the tools in Atlas. After measuring and adding them together we know I replanted a total of 1.24 acres in this field.


My yield data showed yields from 150-200 bushels per acre in the replant areas. Given that the average yield was 175 bushel in replanted areas and areas without replants would have been maybe a modest 25 bushel, we can assume that we created 150 bushel per acre. When I multiply that out (1.24 acres x 150 bpa x $3.75/bu.) we calculate $698 gross earnings. For practicality, we can subtract the cost of seed (1.25 acres x $75/acre = $94). The net result was a positive $604 for the 1.25 acres. In other words, we can be sure it was a $487/acre improvement.

We can be sure it was a $487/acre improvement

My example was done on small areas, however, in some cases other farms may incur much larger areas of damage. This is a simple solution for those who either don’t have precision ag monitors to capture GPS logged data or have somehow lost their records. Not to mention it helps me report to my insurance provider the amount of replanted crops. That helps with my insurance claim and also provides me substantial proof that it was indeed a good decision.

In conclusion…

These are a few ways that the Atlas platform benefited our farm this year. It even provided me additional insights when I compared yield data with it and used the history timeline like a rewind button to track crop issues back to when they occurred. In a way, this is a method of reverse engineering issues back to their origins. However, without timely records that include calibrated data, it would be impossible to compare or trust. With this new tool, we can do it easily and on-the-go.

Excellent insights, Nathan. Thanks for your time.

It was my pleasure.

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