Drone imaging in agriculture applications can encompass a wide variety of specific applications. From simply monitoring for bare soil where nothing is growing, to looking for leaks in irrigation systems, to keeping an eye on water rights. 

But multispectral imaging lets drone operators evaluate actual crop health based on how the crops are absorbing or rejecting different wavebands of light energy. The intensities of this absorption and rejection can be quantified in NDVI, or normalized difference vegetative index, imaging.  

A little about light: How multispectral imaging works

The light we see with our eyes is actually a band of light of differing wavelengths that reflects in the different colors we see in a rainbow. The shorter wavelengths are the violets and blues, with the longer wavelengths being the red hues.

The standard drone camera assembles an image from individual pixels filtered for red, green, and blue light wavelengths. A “multispectral” or NDVI camera has dedicated sensors for those red, green, and blue wavelengths of light so that it can measure the intensity of those wavelengths coming from the plant’s structures.

Comparison of multispectral images of a field

Image 1: Three images of the same field in RGB (left), NIR (center), and NDVI (right). Courtesy of Juan Cantu.

On top of that, there is at least one additional sensor that detects what’s called “near infrared” (NIR) light. This is a form of light that is invisible to our naked eye, but that can provide important information on crop health.

Generally speaking, a healthy plant will absorb more visible light — using it for photosynthesis — and reflect or re-emit more of the NIR, because absorbing too much NIR energy can overheat and damage leave cells. A stressed plant, on the other hand, will reflect more of the visible light and absorb more NIR. Quantifying the percentages of each allows systems to calculate an NDVI value between -1 and 1.

Explaining the correlation of leaf health and light spectrum

Image 2: The visible and near infrared (NIR) light reflected can help indicate plant health.

What do you use multispectral imaging for?

NDVI is actually not a new technology — it’s been available from satellites for over 40 years, so it is a known quantity in the large agribusiness. NDVI is useful because it allows operators to visualize inconsistencies within a field — no crop is entirely consistent, so there will always be areas that are growing better or worse than others. NDVI lets you see that variability sooner, locate it more accurately, and act more quickly than a farmer could by simply driving around the perimeter of a large field.

Drone image of field in NDVIDrone image of field in NIR light Drone image of field in RGB

Image 3: This 230-acre cotton field shows the effect of plugged irrigation emitters, resulting in uneven watering. Courtesy of Juan Cantu.

Once this variability is known, the agronomist can decide how to respond. This response is first dependent on the cause of the stress to the crops — it could be from problems with watering, nutrition, diseases, pests, or any combination of those. Determining therefore NDVI drone imaging is not a replacement for having an agronomist in the field — as with many segments within the drone industry, imagery can only take you so far and needs to be ground truthed. 

For the sake of illustration, let’s say that an agronomist determines that a crop health variability should be corrected by the application of a specific fertilizer. “With advances in aerial application technology, this could be applied as variable rate, with a sprayer applying evenly over a given area, or as site specific application with a drone,” said Juan Cantu, Director of Research & Development for Rantizo

NDVI? Not So Fast…

NDVI imaging from drones is a great timesaver and can provide much greater detail than satellite-based imaging, but that doesn’t mean it’s for everyone. As with other types of specialized imaging like infrared, some training goes into properly interpreting the images you capture.

Just because there’s a lot of green in an image doesn’t necessarily mean your crops are all in great shape. That healthy growth could come from an unintended volunteer crop, or from lots of healthy weeds that will compete with your revenue-generating crops. Similarly, a lot of red in an image could denote plant stress, or bare soil. This is another reason why ground truthing your images is so important.

Speaking of plant stress, there is a lot of talk about NDVI being able to differentiate between types of stress — water, nutrient, pest, etc. But, as Rantizo’s Cantu says, not so fast. “There’s a lot of research still being done in this field. While there are some very promising results, it’s still too early to say this definitively, and more study needs to be done.”

Another thing to keep in mind is the impact of weather and cloud cover. “It would be great if you could look at the same crop in sunny and cloudy conditions and get the same results, but that’s just not how this works,” said Cantu. “If the plants are exposed to less sun on a cloudy day, their absorption and reflection of the available light spectra will be different than on a sunny day.” This is the effect of what’s called Photosynthetic Active Radiation (PAR), or the amount of light available for photosynthesis. Available PAR is impacted by things like cloud cover or shading by trees and buildings — in other words, anything that reduces the amount of sunlight available to plants.

When asked what people thinking about getting into NDVI agricultural imaging should consider, Cantu responded “There is a lot to learn about the imagery itself. The data is very valuable and can make huge, positive differences in an operation, but take the time to learn what it’s showing you first. Also take into consideration what your overall goals are, and if you have the accompanying precision ag technologies that will allow you to get the most out of this imagery.”  

So, if you’re getting started with NDVI imaging, consult with an agronomist or experienced grower to make sure you know what your imaging can and can’t provide. Always make sure your imagery-based assumptions are confirmed on the ground. 

And — as always — fly safe.

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