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  • Writer's pictureEdwin Reidel

Do Genetics Now!

Why RGB imagery may be the best option for your UAS research goals -- and budget

Unoccupied aerial systems (UAS), consisting of a UAV equipped with one or more cameras, are rapidly gaining adoption in outdoor small plot research trials. However, it is still a very new technology and protocols have yet to be standardized. Even the terminology remains unsettled, as demonstrated above. Is it a drone or a UAV? Should we replace the gendered “unmanned” with the neutral “unoccupied”?

Choosing a Camera/Imaging System

Researchers who are thinking of starting a UAS program invariably ask us about he choice of imaging payload. Progeny Drone has made the Plot Phenix app compatible with standard visible light (RGB) cameras that ship with popular off-the-shelf UAVs. With RGB images our customers can perform stand counts, quantify canopy cover, measure row length under vegetation, track crop development over time, and calculate vegetation indices indicative of crop phenology and health.

We also field many inquiries regarding multispectral (MS) imaging systems. These are specialized cameras which can detect light reflected from plants and soils in wavelengths which are shorter (UV) or longer (NIR) than humans can perceive. Still other systems promise to detect variation of canopy temperature using thermal infrared imaging. Plot Phenix supports a wide array of multispectral imaging options from Sony, MicaSense, Sentera, and others. However, when asked to recommend a multi-spectral system we often hesitate to do so, unless the there is a specialized research objective.

This is where “do genetics now” comes into play. As Dr. Katy Martin Rainey, Progeny Drone co-founder and Purdue University soybean breeder, emphasizes to her students, fellow breeders, and crop research organizations (CROs), "There is a LOT of biology and genetics to be done on growth analyses of phenotypes from RGB images." It means that they should adopt technologies which are widely available, proven for collecting a wealth of useful data, easily deployable across multiple environments, and allow the characterization of longitudinal traits. It de-emphasizes bleeding edge technologies which are less proven, more expensive, and offer fewer measurement options. When it comes to UAS in research, Dr. Rainey is “doing genetics now” primarily with RGB (to hear Dr. Rainey describe her UAS breeding program, click here).

What are some advantages of RGB compared to multispectral?

  • Cost: possibly included with the UAV purchase, versus thousands of dollars for some MS systems

  • Resolution and quality: RGB camera and lens systems, having been perfected for consumer products such as cell phones capture beautiful, high resolution images. Multispectral systems are specialized, not mass-produced, and tend to offer lower resolution due to technical and physical limitations

  • Calibration: Not required with RGB, whereas MS imagery must be calibrated for each site and with each change in ambient illumination conditions while onsite.

  • Ease of analysis: Images acquired with MS come in either narrow visible wavebands that are harder to evaluate by eye (think of red LEDs on crops) or they are acquired outside the range of our vision and must be falsely rendered in grayscale for us to “see”

Wait, what about NDVI?

The Normalized Difference Vegetation Index (NDVI) measures reflected light in two wave bands (or channels): Visible Red and Near Infrared. It was developed in the 1970s for detecting the presence and “condition” of terrestrial vegetation versus bare soil from satellite images. Since then a wide variety of vegetation indices have been developed by remote sensing researchers and adopted for agriculture. NDVI is particularly useful for detecting the difference between green and senescing leaves. The index has gained widespread popularity in agriculture despite the limitation that it saturates as crops green up.

Graphical Portrayal and Formulas for NDVI and GRVI
NDVI and GRVI Indexes

As an alternative to NDVI, we offer many visible light-based vegetation indices with Plot Phenix. For example, the Green-Red Vegetation Index (GRVI) takes advantage of the fact that visible light cameras are in a sense basic multispectral imagers. Using an array of filters, information is collected separately for red, green, and blue wavebands (thus, RGB). While these data are typically combined to produce a recognizable photographic image, the three channels can also be analyzed separately. GRVI uses the relative intensities of reflected Green and Red light from canopies to quantify canopy greenness and estimate vigor, much like NDVI.

To conclude, when planning your UAS data collection program we encourage you to consider RGB first and foremost. For the price, ease of use, and range of metrics it cannot be beaten. If you wish to do multispectral imaging, it should be in conjunction with RGB and it should not slow you down from getting your program started.

Any questions about cameras, sensors, or other hardware? We are always available and happy to assist.

Now, go do genetics!


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