Drones in Agriculture and Farming

These aircraft are equipped with an autopilot using GPS and a standard High Definition camera controlled by the autopilot; software on the ground can stitch aerial shots into a high resolution mosaic map. This low-altitude view (from a few meters above the plants to around 120 meters, which is the regulatory ceiling in the UK for unmanned aircraft operating with special clearance from the CAA) gives a perspective that farmers have rarely had before. Compared with satellite imagery, it’s much cheaper and offers higher resolution. Because it’s taken under the clouds, it’s unobstructed and available anytime. It’s also much cheaper than crop imaging with a manned aircraft, which can run £1,000 an hour.

Drones can provide farmers with three types of detailed views. 

First, seeing a crop from the air can reveal patterns that expose everything from irrigation problems to soil variation and even pest and fungal infestations that aren’t apparent at eye level. 


Second, airborne cameras can take ​​​​​​​ NDVI images, capturing data in RGB , which can be combined to create a view of the crop that highlights differences between healthy and distressed plants in a way that can’t be seen with the naked eye. 

Finally, a drone can survey a crop as often as you need. By doing so regularly that imagery can show changes in the crop, revealing trouble spots or opportunities for better crop management.​​​​​

It’s part of a trend toward increasingly data-driven agriculture. Farms today are bursting with engineering marvels, the result of years of automation and other innovations designed to grow more food with less labor. The implications cannot be stressed enough. We expect 9.6 billion people to call Earth home by 2050. All of them need to be fed. Farming is an input-­output problem. If we can reduce the inputs—water and pesticides—and maintain the same output, we will be overcoming a central challenge.



This is a NDVI (Normalized Difference Vegetation Index) image

NDVI is an index of plant “greenness” or photosynthetic activity, and is one of the most commonly used vegetation indices. Vegetation indices are based on the observation that different surfaces reflect different types of light differently. Photosynthetically active vegetation, in particular, absorbs most of the red light that hits it while reflecting much of the near infrared light. Vegetation that is dead or stressed reflects more red light and less near infrared light. Likewise, non-vegetated surfaces have a much more even reflectance across the light spectrum.​