Measuring flowers to help predict yield
Because of its sensitivity to the presence of flowers, the blue band is at the heart of flower counting or bloom density analysis. Flower count is notably important for fruit-tree growers since it has a direct link to yield. Flowers are the reproductive structure of flowering plants and will eventually become fruits after fecundation. Therefore, the bloom density of a tree can be an indication of how fruitful the subsequent harvest will be.
To demonstrate how the blue band plays a key role in flower count and yield prediction, let’s take a look at the following imagery of cranberry vines provided by Ocean Spray. The most critical factor in determining yield is the amount of fruit produced from a given number of flowers. Cranberry flowers are not capable of self-fertilization; their pollen structure is too heavy to be carried by the wind. Thus, insects are required to move pollen from flower to flower. To become more attractive to insects, cranberries produce more flowers than the number of fruits they can bear. By having an estimate on the number of flowers on a given plant, a grower will be able to determine the number of pollinators needed and eventually predict fruit set and yield.
The RGB image above shows a complex canopy in terms of color. There are red and green leaves as well as white flowers. Looking at the red or green bands, the flowers and leaves are indistinguishable. But when looking at the blue band, the flowers are bright and easily spotted, making it easier to count them.
This type of analysis is not only applicable to cranberries, but to countless other flowering crops. It enables bloom density analytics and gives the grower the opportunity to make more informed management decisions guided by actionable information.
RGB composites, additional layers, and scouting
In addition to bloom density analytics, the blue band — combined with red and green bands- enables generation of a true color (RGB) composite, which is highly valuable during crop scouting or as a reference for other analytical layers. A true color image is often the first thing an analyst looks at to find context, as it is easier to identify objects and interpret features in RGB than in individual bands or color-infrared composites. Without a blue band, this true-color image cannot be generated.
Not only is an RGB image important for scouting, but it’s also important for analysis. A perfectly pixel-aligned RGB image can be combined and compared with other information layers to achieve better classification of soil, shadows, and vegetation.
The blue band is also key to generating vegetation indices like EVI (Enhanced Vegetation Index), GLI (Green Leaf Index), and VARI (Visual Atmospheric Resistance Index). This last one, minimally sensitive to atmospheric effects, is notably useful when comparing flights over time under different climatic conditions.
Use all five of your senses
Overall, a multispectral sensor with a blue band enables the collection of a wider range of information, allowing for more complex analytics. Sensing with the blue band — in addition to other commonly used bands — is like using all five of your senses, as opposed to just two. Just as your five senses allow you to have a complete idea of your surroundings, the blue band helps to give a broader, more accurate picture of what is going on in a crop.
If you are already using a two- or three-band sensor, imagine all the things you might discover with a five-band sensor that includes the blue band!
To view the different vegetative indices available in Atlas, check out our example data set. If you have a case study with RedEdge, Sequoia, or Atlas that you would like to showcase, please send us an email here.