Use Case: Measure Growth Stages With Time-Based Metrics
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 measure crop development and identify growth stages with dynamic, time-based metrics.
A lightly edited transcript follows.
I'm going to give a couple of examples here briefly on growth stage measurements, which are important for a couple of reasons. Some inputs are designed for specific localized growing environments, and timing of inputs is critical relative to the growth stage in some crops as well.
What we've done is developed some time-based metrics that are looking at how crops are developing to provide measures on the plant growth stage.
Learn more: Analyze Stand and Vigor With Aerial Phenotyping
Canola Flowering Dynamics
The first example is a canola trial. Again, small plots. In this particular case, we're using the spectral signature of canola flowers to measure the flowering fraction development in each plot.
In this particular heat map at the center, which is percent of flower, green shows a higher percentage of flowering and red is less. The time series at bottom shows the flowering development for the selected plot that's shown at top right.
The ability to track flowering development in canola is particularly important for the timing of inputs. It’s also used as a metric for yield forecasting.
One of the takeaways here is the ability to isolate specific variables for measurement is one of the critical aspects of using aerial measurement techniques like this.
Learn More: Quantify Crop Response to Treatments & Inputs
Soybean Maturity Ratings
The next example in the next slide is a soybean trial. Soybeans are bred for different growing environments based on the length of the season. Historically obtaining soybean maturity ratings, as they're called, is an extremely labor intensive and subjective process.
Ultimately, what we're trying to do here is breed seeds that are going to optimize yield based on the length of season.
Again, similar layout and the data you're looking at here. The time series at bottom is the measure of maturity for the selected plot. What we're showing is that in the pop up, the data allows us to actually measure the days to maturity, which in this case was 121, and the date that that maturity was reached.
Learn more about how SlantRange Aerial Phenotyping provides the insights for researchers to quantify and predict crop performance in this recent article.
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