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Using drones for agriculture is a hot topic these days, and for good reason. These unmanned aerial vehicles (UAVs), as they are sometimes called, are rapidly becoming a core tool in a farmer’s precision equipment mix.

Today’s farmers have to deal with increasingly complex concerns. Issues such as water – both quality and quantity, climate change, glyphosate-resistant weeds, soil quality, uncertain commodity prices, and increasing input prices to name a few.

Growers are turning to high-tech tools, often under the banner of precision agriculture, to respond to and mitigate these and other concerns. Precision agriculture divides a field into zones that can be individually managed with a range of GPS-equipped precision machinery. Technology enables farmers to collect, store, combine and analyze the layers of data that drive precision nutrient and irrigation management.


Drones collect information largely based on the light reflected by the crop below. For agricultural purposes, using a specific type of sensor can help growers collect data that indicates where issues exist so that they can take appropriate action. Plants, of course, capture visible light to drive photosynthesis. However, near infrared (NIR) photons don’t carry enough energy for photosynthesis but they do bring lots of heat, so plants have evolved to reflect NIR light. This reflection mechanism breaks down as the leaf dies. Near Infrared sensors take advantage of this property by monitoring the difference between the NIR reflectance and the visible reflectance, a calculation known as normalized difference vegetation index or NDVI. A strong NDVI signal means a high density of plants and weak NDVI indicates problem areas on the field.

Sensors can be as simple as a camera to take video or still photos from above a field, which may assist in spotting some problems. To really obtain value from an agricultural drone, however, other types of sensors must be considered, as well as tools to fly the drone in a pattern over the entire field and software to combine the sensor readings across the field into a single layer that is then analyzed and geo-referenced. Only in this format can a user then use a GPS-enabled smartphone or other device to walk to and inspect specific problem areas or combine the information with other data layers.

There is a lot of research activity focused on using other types of drone-mounted sensors for agriculture. The two most commonly mentioned are thermal cameras and hyper-spectral cameras.

Thermal sensors can read the radiated temperature of an object, and some of the newest models are light enough to be carried by a small drone. A thermal sensor might help identify how plants are using water, as those with access to more water appear cooler in an image. The challenge is that these temperature variations are minor and can be difficult to distinguish from the other factors that might heat or cool the plant, such as breezes, sun exposure, etc. More research is needed in this area.

Hyper-spectral sensors record many wavelengths of both visible and invisible light. Although the size and price of these cameras are coming down, they are still large and expensive. The promise of these sensors is that they might be able to identify the specific type of plant merely by measuring the color of light that it reflects, which would make it easy to pick out things like herbicide-resistant weeds. However, calibrating these cameras to work on a low-flying drone in a farm environment where the light conditions vary as much as they do is a problem that needs to be solved before hyper-spectral cameras can deliver.

Allow us to assist you in determining the best solution for you.


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