Use Case: Analyze Stand and Vigor With Aerial Phenotyping
In a recent webinar, SlantRange CEO Michael Ritter shared how Aerial Phenotyping technology gives crop researchers the insight needed to quantify and predict input performance.
The short, 3-minute video below highlights how researchers can use early-season stand counts and measure seedling vigor to assess performance across different input varieties and treatments. A lightly edited transcript follows below.
In production, what you want is a quick assessment to make an input decision. For example, do I need to make a replant decision?
What we want to understand on the input supply side is to quantify seed or seedling vigor and assess performance across different input varieties and treatments.
Another really critical aspect here is achieving separation of variables, so that at the end of the season, when you're looking at yield, you're able to separate what contributed to yield, how much of it was due to stand in early-season versus what might have been treated or happened later-season.
There's multiple steps involved here. First step, of course, is classifying, segmenting, and measuring individual plants in the imagery. Then we can produce a number of metrics like plant count; plant size statistics; emergence fraction; vegetation fraction, which deals with canopy structure; and weed metrics.
This is basically what the user interface looks like on a small plot trial of corn at about V2 stage. What we see here is, the central area of is a satellite background image with a heatmap overlaid.
Here, the variable that we're looking at happens to be plant size in square centimeters. Green are larger plants, red are smaller plants. And then gray areas, black areas are little to no stand at all.
Each pixel color represents the median plant size within that five-meter-by-two-meter plot. At the bottom left, there's a histogram of the plant sizes across all the plots. At the bottom right is an image chip of a single plot with green overlays for the corn plants, and then red overlays for weeds, which are a little bit difficult to see, but they're at the top end of that plot.
At the top of the frame, there's some buttons to select different data variables that were collected at the same time: emergence fraction, plant count, weed count. This is a user interface that is helpful for visualization.
All this data is available for download or API interface to pull into the input supplier's own analytics or their own platform.
So this approach is a little bit different from the standard counts that we see in production agriculture. When we're aiming for less than 1% error, what that means is in a plot that might have 70 plants in it, we can exceed our error budget if we miscount a single plant.
So it's absolutely critical we identify and properly classify individual plants for measurement. The spatial accuracy is required as well. If you misplace that aerial measurement by 10 or 20 centimeters, you might start overlapping another plot, and then once you do that the results become unusable.
All those features combined allow you to really quantify stand across large-scale trials like this.
Learn more about how SlantRange Aerial Phenotyping provides the insights for researchers to quantify and predict crop performance in this recent article. To learn more about our technology and applications, download our product guide.