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5 Actual Uses For Drones In Precision Agriculture Today

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Having covered the ag retail industry and drones in precision agriculture for CropLife.com and PrecisionAg.com (shameless plug) over the past three years, I’ve heard a lot at this point on the subject of drone applications on the farm.

From the ability to image, recreate, and analyze individual leaves on a soybean plant from 400 feet, to getting information on the water-holding capacity of soil, to variable-rate water applications out West, the industry has been sold -and sold HARD- on how UAVs can deliver ROI for both growers and crop consultants alike.

Unfortunately, many of the promises being made to our nation’s food suppliers simply cannot be delivered or backed up by proper research… yet. The FAA is just now opening up segments of the national airspace for commercial research to take place.

In the coming years all of the possible uses for these flying robots will be fleshed out by the industry itself, but for now here are five applications that are already being implemented on someone’s Back Forty, somewhere (as long as FAA doesn’t find out):


Mid-Season Crop Health Monitoring (aka Scouting): The ability to inspect in-progress crops from on high with Normalized Difference Vegetative Index (NDVI) or near-infrared (NIR) sensors is, thus far, the #1 use for drones in farming. A task that traditionally was done by often-reluctant college interns walking fields with notepad in hand, drones like SenseFly’s eBee Ag now allow for coverage of more acres, as well as the capturing of data that cannot be seen by the human eye (NDVI). Plus, it removes much of the human error aspect of traditional scouting, though physically inspecting areas of concern after viewing the imagery, is still recommended.

Irrigation Equipment Monitoring: Managing multiple irrigation pivots is… well, it’s a pain, especially for large growers that have many fields spread out across a county or region. Once crops like corn begin reaching certain heights, mid-season inspections of the nozzles and sprinklers on irrigation equipment that deliver much-needed water really becomes a pain-in-the-you-know-what.

Mid-Field Weed Identification: Using NDVI sensor data and post-flight image processing to create a weed map, growers and their agronomists can easily differentiate areas of high-intensity weed proliferation from the healthy crops growing right alongside them. Historically, many growers haven’t realized how pronounced their weed problem was until harvest time.

Variable-Rate Fertility: Although many will argue ground-based or satellite imagery, along with a dedicated grid soil sampling program, are more practical for the purpose of refining Nitrogen, Phosphorus and Potassium applications in agriculture, drones do have their fit. Agribotix, a Boulder, CO-based ag drone service startup, has used NDVI maps to direct in-season fertilizer applications on corn and other crops. By using drone-generated, variable-rate application (VRA) maps to determine the strength of nutrient uptake within a single field, the farmer can apply 60 pounds of fertilizer to the struggling areas, 50 pounds to the medium areas, and 40 pounds to the healthy areas, decreasing fertilizer costs and boosting yields.

Cattle Herd Monitoring: Many growers during the days of depressed commodity prices in the late-90s to early 2000s made the call to diversify their farms by adding cattle or swine operations. Drones are a solid option for monitoring herds from overhead, tracking the quantity and activity level of animals on one’s property. And they are especially helpful for night-time monitoring due to the human eye’s inability thus far to evolve to the point of seeing in the dark.

So there you have it. As the calendar turns to 2015, these are the current most-common uses we are seeing for drones in precision agriculture. Of course, we do expect this list to undergo explosive growth in the near-future as more and more research takes place, so stay tuned to DroneLife.com and our ongoing coverage of drones in precision agriculture.

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