Why the Bungee Cord!?

Over the past year, many of my clients have been asking me why we use a bungee cord to launch our fixed wing Hornet drone here at Agribotix.  Why not make a drone that can be hand launched? - they ask.  Some have even suggested that a catapult launcher might be more convenient for field operations.  I've had a lot of experience with catapults and even built my own; one of which can be viewed here. Catapults offer consistent, reliable launches but with a price: catapults are expensive because they condense several pounds of tension into about a four foot span that a bungee would normally spread over ±40 yards; catapults need to be well built to withstand the stresses they endure, and even then they often break down.  Hand launches on the other hand are unreliable and often result in crashes due to operator error.  I remember this well from my time in the Army where I saw a lot of this happening.

But assuming we are only working with drone professionals who know how do a hand launch properly (and also know better than to put the propeller on backwards) there is yet another reason not to go with the hand launch: doing so limits the range of your drone!  This is because in order for your drone to be hand launched, it needs to have a wingspan that can produce adequate lift for takeoff at slower (hand launching) speeds.  Once that same drone is in the air at cruising speed, its wingspan will produce excess drag, this is why manned aircraft have flaps.  Flaps produce the extra lift at takeoff and landing needed to maintain flight at lower speeds but retract once the plane is cruising in the air so as to reduce the drag of the wingspan.  Although these work well on larger airframes, they tend to complicate the design of sUAS systems to a point where they become more of a burden than an asset.

Bungee cord launches offer a happy medium between the heavy costs of catapults on the one hand, and the limiting range (and crash potential) for a hand-launched drone on the other.  No matter how much battery weight your hand-launched drone is currently carrying, it could carry more and go farther if it were bungee launched.  We now have a video here showing how to use our bungee cord and how it is put together (1:30) -to be sure, there is a short setup time of about 8-12 minutes, but we feel this is a small drawback when considering the alternatives discussed above.

 

How do I optimize the quality of my drone imagery?

 

The imagery Agribotix can produce for a given customer is only as good as the provided data; poor data will produce poor results. Below are three of the most common problems with stitched imagery and a lengthy discussion of what causes each of them and how to fix them. These problems are cloud and sky shadows, stitching artifacts, and GPS failure. If your imagery results are less than satisfactory, there is a good chance that your problem is one of the three listed below.

Cloud and Sky Shadows

Cloud shadows appear when the cloud cover overhead blocks the sunlight at varying degrees while the drone is in the air taking photos.  Cumulus clouds moving past the sun will create the most noticeable cloud shadows.  When the sky is overcast but the overcast clouds vary in thickness as they move across the sky and block the sun, they too will create cloud shadows.  Ideal weather conditions for drone photography are either clear skies or 100% overcast with a consistent overcast cloud layer.

The telltale signs of cloud shadows are:

1.) Broad, consistent transition from "good" crop health to "bad" crop health as if the bad areas were airbrushed onto the false color map using Photoshop.

2.) Consistent "bad" area that moves across several crop fields of varying species and growth phases as well as natural foliage.  These "bad" areas do not conform to any apparent geographical feature such as topography or soil type, nor do they conform to row direction, vehicle tracks, or irrigation layouts.

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Additionally, it is possible to have the opposite effect of what we call 'sky shadows' - these occur when the drone is flying on an overcast day but the sunlight manages to penetrate through the cloud deck for short periods of time during the drone's flight.  These appear as good-healthy airbrushed patches on a crop field rather than bad ones.

Below are examples of cloud shadows from a photo set; note the otherwise bright pink canopy interrupted by blotches of dark pink. The dark pink areas have a blocked view of the sun. IMG_1296.JPG in the middle of the bottom row has no sun shadow at all. IMG_1291.JPG on the upper right-hand side is almost entirely covered by cloud shadow except for the upper left-hand corner of the image. If you have images such as these in your photo set, you should remove them before uploading them to Agribotix for processing; if you have too many images removed from the set it may result in your final stitched image having holes within it but out experience has been that such holes are better than the false information provided by sun and sky shadows.

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Spotting cloud shadows in final results is part art, part science; at Agribotix we've gotten good at it after ground truthing dozens of fields over the last two years; when we see them in your imagery we will try to point them out but the customer is ultimately responsible for raw data input and final interpretation of results. The best way to avoid cloud and sky shadows altogether is of course to always fly when you have clear skies, or with clouds that are not blocking the sun at the very least.

 

Stitching Artifacts

Stitching artifacts are characteristics on your final stitched image that appear to show differentiation in crop health but in reality are non-existent on the physical ground and are mere by-products of the image processing. Telltale signs of stitching artifacts are:

  1. Regions of your false-color map changing colors as you zoom in and out on Google Earth.

  2. Visually apparent differences in resolution on the CIR stitched image.

  3. Several areas (all roughly the same size) that appear to be 'plastered' onto the image.

These artifacts can appear in part of your field or across the whole field. Stitching artifacts are highlighted in the red boxes below.

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Stitching artifacts can be caused by fast moving clouds that produce cloud shadows over a particular geographic spot in one photo, but not in subsequent photos; this confuses the stitching software as to whether or not such a spot is supposed to be dark or light. Varying degrees of focus between individual photos taken by the drone's camera will also lead to stitching artifacts as well as poor stitching. Below are two photos; the one on the left has less focus than the one on the right; whatever your camera's focus quality is, it should be consistent, otherwise photos like these will produce stitching artifacts.

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Below is a photo set taken with a camera that had poor automatic exposure control; note the four sets of three dark photos highlighted in red boxes. If you have images like this in your photo set they should be removed before sending the remainder to Agribotix for image processing, these too will produce stitching artifacts.

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Another cause of stitching artifacts is not enough oversampling. Agribotix recommends an oversampling of 10; that is, each point on your field should be in (at least) 10 different individual photos from your photoset. Many customers get excellent results with an oversampling of 6.5, but their fields are small and the stitching software has several reference points around the field edges to work with when aligning the photos. Orchards and vineyards need less oversampling because the stitching software will find reference points within individual trees & vines with which to use. Below is an individual photo taken over a wheat field; notice the general uniformity in color/texture -it is extremely difficult for the stitching software to find individual reference points in this almost featureless terrain and therefore needs an oversampling of 10 to align the photos into a single orthorectified mosaic. Notice the false color image on the lower left; the center of the image is green whereas the outer edges are grey/black. The original infrared image is on the right and is brighter in the middle and darker around the edges. This phenomenon is known as 'vignetting' and is common in photography. The important thing to remember is that having a low oversampling by itself is one thing, but when oversampling is coupled with a photo set wherein all or most of the photos contain noticeable vignetting, the result will be severe stitching artifacts throughout the stitched image.

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The final cause of stitching artifacts is when the aircraft altitude varies greatly. Below are two images; one from the drone's takeoff and another from the drone's landing approach, since these are taken at an altitude far below the cruising altitude of the drone's lawnmower flight pattern, they will produce high-resolution patches of the stitched image leading to a stitching artifact. The Agribotix Field Extractor will automatically ignore images taken below a certain altitude above ground level in order to avoid this problem entirely. If you are not using Field Extractor you should review your photoset and remove such images before uploading them to Agribotix for processing.

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Below is an example of a field that is completely covered in stitching artifacts; there is no actual difference between those areas shown to be generally green and those generally yellow. Note the large black holes on the upper end of the image caused by flooding; just because stitching artifacts are present doesn't mean your imagery can't tell you valuable information about your fields; the key is to recognize stitching artifacts when they manifest and not let them lead you to false conclusions.

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GPS Failure

Below is an example of what happens when your drone system's GPS sensor fails to write accurate geo-coordinates to your camera's individual photos in a process called geotagging; the final stitched image will not properly overlay onto Google Earth and will often be the wrong size or have an inaccurate orientation.

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While some cameras have their own internal GPS sensors, these sensors are less accurate than those embedded in a drone's flight navigation system. The Agribotix Field Extractor uses the GPS data pulled from the drone's navigation system to optimize the georeferenced results.