Measuring Flowering in Canola

The ability to measure and understand flowering dynamics adds valuable new management insights for canola researchers and producers. 

Between 40-60 percent of open flowers turn into productive seed pods, and the plant can adjust yield based on the number of flowers that are produced and pollinated — making this metric useful for estimating yields. Beyond the percentage of green buds that become yellow blossoms, knowing the date of the first bloom and the trajectory from first flower to full bloom is crucial information for understanding the results of research trials. Furthermore, flowering stage provides important guidance for the application of pest management inputs. 

Our Aerial Phenotyping platform allows researchers and agronomists to measure the onset, development, and ripening of canola flowers using multispectral imaging techniques from low-flying drone aircraft systems. The examples below show imagery, the detection of blossoms, and how this is represented in our platform. 

Example 1: Development of Canola Flowering

Dataset 1 - edit

The examples above are RGB (color) images of two canola plots collected with the SlantRange 4P+ multispectral sensor system from early in the reproductive stage through onset of flower development. In the six days that elapsed between the second and third images, flowering has increased rapidly. 

While the color images above are useful, to extract quantitative measures of flowering statistics, we employ the narrowband spectral channels of the SlantRange 4P+ with SlantRange's “Smart DetectionTM” processing algorithm. Smart DetectionTM works by finding pixels within imagery that are statistically proximate to a predefined signature or characteristic. In this case, the color and size of canola blooms are quite easily detected, classified, and segmented. Once segmented, downstream algorithms can calculate percent bloom, area coverage, or other metrics. The images below show where the algorithm has detected canola blooms with a blue overlay.

Dataset 1 - smart detection - side by side

In this case, showing the same two plots in each image, has increased from 6.6% to 19.1% flowering fraction (a 2.9x increase) in 6 days. The plot on the right has increased from 8.8% to 22.4% (a 2.5x increase) over the same period. 

Example 2: Canola Flowering to Ripening

Our second example shows the progression of flowering to ripening in the images from left to right below.

Dataset 2 - side-by-side  
Dataset 2 - Smart Detection - side-by-side
Using the same 4P+ narrowband spectral channels and Smart DetectionTM techniques, we observe flowering fraction decrease from 29% to 13% to 3% to 0% for the lefthand plot over approximately a weeklong period. The righthand plot similarly decreased from 28% to 12% to 4% to 0%.

Inside SlantRange's Aerial Phenotyping platform (below), the complete time history of multiple metrics are captured in addition to other statistical information. An aerial view of the entire trial is shown at left with a color overlay of the selected metric. At right, a zoomed-in view shows individual plots in greater detail. At bottom left, a histogram shows the status of all plots on the selected measurement date while the line plot shows the time history of the single selected plot over the whole season.

SV-C-Cap-3

 

From here, it’s easy to export your metrics in a variety of different formats. API integrations are available so you can import statistics into your own database as well.

If you’d like to learn more about the technology behind our Aerial Phenotyping platform, along with how it can upgrade your research and breeding trials, don’t miss our recent webinar. To learn more about measuring flowering in canola, or how we might be able to help your organization, get in touch.  

Editor’s note: We value the confidential nature of our client data and the sensitivity of their research programs. Consequently we limit the details we show publicly. The examples above span multiple, unrelated trial locations.