The June 2016 update to the FAA’s Part 107 regulations for flying drones is just one factor contributing to the increase of unmanned aircraft systems (UAS) use in several industries, including agriculture. But that’s only half of the story. As anyone in business knows, in order for a tool to make sense on a worksite, it first has to make sense on the bottom line.
In order to see a return from a drone investment on your farm, you need to know how to use a drone to save time, improve efficiency, and increase yields. Then, the real key to unlocking the true value of a drone comes from understanding the technology behind it.
The high-quality images produced by drones are used for everything from pre-season scouting to monitoring crop health to identifying equipment issues. Drones produce three common image types:
-RGB images are similar to photos from a regular camera. They’re easy to understand, even for the novice drone user, but are the least descriptive of the three types.
Near Infrared (NIR)
-NIR provides images with higher levels of detail than those produced by RGB by utilizing color bands outside the light spectrum visible to the human eye.
Normalized Difference Vegetative Index (NDVI)
-NDVI uses both visible and near-infrared sunlight reflection to measure biomass (vegetation). Similar to NIR imagery, NDVI provides a higher level of detail than RGB images.
Each of these image types play an important role in the various applications for which drones are used.
The coverage area, vantage point, and speed a drone provides makes it a great tool for pre-season scouting. Using standard RGB imagery, the drone can produce 3D maps used for soil evaluations, topography reviews, and identification of drainage issues.
By gathering, reviewing, and evaluating this mapping data prior to planting, you only have a complete view of the whole area, but you may be able to identify problems and adjust planting strategy before, rather than during, the season.
The primary advantage of drones over a manual scouting process is speed. An area normally monitored by a crop scout in several hours can be covered in a single, quick drone flight. This allows for one of the most common uses of drones in agriculture – ongoing monitoring of crop health throughout the season. NIR imagery is most valuable in this process for several reasons.
First, NIR images show heat so they can easily identify areas of plant and water stress. Their high level of detail offers additional applications such as weed detection, defining management zones, evaluating effectiveness of ponding and water management, and quantifying machinery-induced crop limiting factors. This ability to identify concerns and intervene quickly is directly linked to a better year-end harvest.
There are uses for RGB images in-season as well. They’re often used to identify planter skips and evaluate areas of lost production, allowing you to correct the problems.
In addition to their immediate help before and during the season, drone use can be beneficial over long periods of time. Like RGB and NIR, NDVI images can also show ponding, help assess crop vigor, and show changes in field conditions over time.
NDVI images measure the amount of biomass or “greenness” of a plant and create an index, which is then compared to areas of less vegetation and more vegetation. The numbers range from -1 to +1, with high amounts of biomass and green vegetation having increasingly positive numbers.
NDVI values are very sensitive to anything that affects light, such as haze, clouds, or even soil. For this reason, NDVI images are most effective in optimum conditions.
Bottom line: A drone is a helpful tool that can provide quality data and images but it’s up to you to analyze data and use it to make the best decisions for the crop and your farm.
About The Author
Nate Dorsey is an Agronomist for RDO Equipment Co. and based in Moorhead, MN.
Contribution to this article provided by Matt Hayes, Mapping Product Supervisor, and Bill Edmonson, UAV Product Specialist, both for RDO Integrated Controls and based in Billings, MT.