Drones in agriculture are always ready to fly at a minute’s notice. With efficient data collection and automated data processing software, you can get the big picture within hours instead of weeks.
Compared to traditional satellite imagery or manned aircraft surveys, drones can fly much lower and slower. Large sensors and precision optics offer unparalleled image quality and accuracy.
The higher resolution imagery produced by drones has opened up a whole new arena for data analytics. From crop stand counting and tree crown delineation to plant height and multi-spectral indices.
Tired of chartering ground survey teams or waiting to book expensive manned aircraft for crop scouting? With quick-snap components and minimal planning logistics, drones can be deployed in minutes to scout potential problem areas, giving you information to make the better decisions. When early detection and fast analysis is key, there’s no comparison to drones in agriculture – Better data, more information, less time.
From TVs and laptops to cell phones and tablets, everything is high-resolution, your data shouldn’t be any different. The days of Google Earth analysis are over and the high-resolution era has made its mark on the agriculture industry in a big way. Altavian is serious about the potential for drones in agriculture, utilizing full-size DSLR cameras and high-quality lenses to ensure no competitor can compare to our optical quality. From a standard 2.6cm resolution flight all the way down to 5mm resolution, every image is as clear as the last.
On a standard medium-area flight, Altavian drones collect data at 1.3cm resolution. Meaning every pixel represents 1.3cm² of area on the ground.
For a manned aircraft to compare to the efficiency cost of a drone over the same area, it would have to collect imagery at 10cm resolution.
Start off by scanning your field with raw color infrared (CIR) imagery using a Fusion payload.
Transform your CIR imagery into an NDVI map to see relative health of your plants in a color ramp.
Convert your NDVI map to a grid index map and import it directly into your tractor to save money.
With well over 100k acres of agriculture data collections under our belt, Altavian has developed and adapted a complete product line to support every kind of agriculture data project. From efficient large-area coverage with the Nova F7200 to ultra-high-resolution scouting with the Galaxy R8700. With modular payload bays in both platforms, the Fusion payload series offers an adaptable suite of data collection options across the visible and infrared spectrums. Meanwhile, the Flare software provides advanced planning and flight controls for both platforms with a user-friendly interface, showing real-time coverage quality and data collection assurance.
Inspect the relative health of your crops without sacrificing on image quality. NDVI maps provide a quick comparative analysis of your plants at resolutions unmatched by traditional index calculation methods.
With new computer vision image analysis algorithms, long-sought metrics such as stand counts can be accurately derived and repeated. Evaluate your planting techniques and estimate yield long before harvest.
Whether you’re analyzing drainage considerations or calculating crop yields, terrain models provide an intimate insight into your fields’ characteristics. Take advantage of high-precision drone data to examine your field in 3D.
The precision agriculture industry has long relied on geographic data to catalog and compare crop performance over time, traditionally utilizing vector data and low-resolution raster data files. After years of testing and analyzing the wide landscape of familiar agriculture software programs, Altavian takes pride in assuring the widest compliance in any existing software workflow, ensuring our data will integrate seamlessly without needing to learn new software.
One of our operators deployed to the great plains of the Dakotas to train new operators and documented the challenges of high-wind operations. As a licensed full-scale pilot and award-winning RC competitor, his notes on the performance of our unmanned systems proved a rather interesting comparison.