We have been very busy at Aero Hawk in developing an intuitive software tool that is easy to use and gives proper non-diluted picture placement (The Most Important Pixel) and readings that make data usable. We have named the Most Important Pixel to our picture placement approach of giving more importance to the pixels closer to the center of each picture for a precise pixel reading. Therefore, we are not using blending techniques to approximate pixels among picture boundaries. At Aero Hawk we are doing our utmost to get the Ag industry to have a great experience using drones to survey their crops. We have just released this new software tool that does picture layout (Not Stitching) as we feel like there is much lost in stitching as far as usable data. Thus, we are using geo-referenced tie points and the Most Important Pixel approach for picture placement on a web map service. This version does most Indexes that are available through Multi-Band Filters. We are using Multi-Band Filters in our cameras so there is an easy perfect overlapping for vegetation indexes estimations and overcome the alignment issue that represents the use of single cameras for each narrow band filter. We have pre-calculated over 30 Indexes that give different actionable items. We will talk more about cameras and filters later. There will be new releases coming out regularly and we intend to stay ahead of the curve so everyone using the software has a great experience. For more info go https://aero-hawk.com/product/aerohawk-maps/.
The Most Important Pixel
Dr. Hernandez from Aero Hawk has developed a way to view what we would call the “Most Important Pixel”. His algorithms he developed for placing pictures together has made it so the areas of the field that need attention don’t get overlooked because of pixel saturation from other pictures. This alone is an immense breakthrough for finding disease early in plants. In researching and using almost every stitching program out there, I am starting to think that because stitching is also about the use of blending techniques for mixing pixels from pictures next to each other, we are getting a misrepresented picture reading of the crop. That is, when a bunch of pictures are stitched and blended together the actual exact reference point of a problem can be skewed. For instance, if I have between 120-1000 pictures. (depending on the camera resolution) on a 160-acre field, about half of those pictures are stitched and blended with their neighbor picture on all 4 sides. Every stitch line is a distortion area that may have a bad result and with an 80% overlap, that line has become wide enough to distort important pixel values in the whole picture. If you are looking for disease in a field, that is leaving room for lots of errors. We believe that if there is a way to place but keep the integrity of the picture on a grid so then we can zoom into every picture on that grid so later on the analysis, we can get an exact reading of each individual picture that is not prone to image processing errors (see figure 1). We will eliminate problems that I see with a beautifully stitched picture that might have errors under the surface that are unseen. If we can see pixels that are a problem, let’s say it is a 5-square inch area (32^2 cm) but if the overlaying pictures are off by 2.5 inches all the telling pixels get diluted. In many cases, there will be up to 6 overlays of the same area so again we get that problem area is diluted and now we don’t see any problem because stitching caused the area to be diluted to look like everything around it.
Figure 1. Pixel-based vegetation index visualization in Aero Hawk Maps
What is Next…
The next software release will include actionable data tools that would help to improve decision making in farm management. Some of these actionable data tools will be the highlight of areas that could represent a problem in the field, nitrogen map estimation, and crop yield estimation.
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