High Throughput Phenotyping Sensors


The red-green-blue (RGB) sensor. RGB sensors pick up wavelengths of light in the red-green-blue spectrum to create a picture much like how you and I see the world. An RGB sensor mounted on a UAV will take enough pictures throughout the entire flight with enough overlap from one to the next so that the researcher can create an image of the entire field during the data processing stage. The final product will look similar to Fig. 8 on the right. These images can be used to create what is called a shapefile as well, which we will discuss more in depth during the data processing section.

Figure 8. RGB mosaic of a wheat breeding field. A compilation of hundreds of aerial photos results in a large section of a field

Created by C. Mick using Pix4D software, 2022.


Multispectral sensors collect the same data as RGB sensors and are also sensitive to NIR wavelengths. The advantage to this is that a multispectral sensor can provide a picture of the entire field (like an RGB sensor) but can also be used to detect differences in NIR reflectance (see Fig. 6, previous section). These NIR differences can provide additional information about the crops vegetation by calculating the vegetation indices. These vegetation indices are useful for understanding plant health and biomass.


Thermal sensors are sensitive to wavelengths in the infrared region. Because infrared wavelengths are undetectable to the human eye, thermal sensors can identify plant stress before humans can. This makes them useful when it comes to water stress and disease detection.


LiDAR stands for light detection and ranging. LiDAR is known as an active sensor meaning it sends out a pulse of light which gets reflected to the sensor. LiDAR measures how much time passes between emitting the light and receiving it back. This measure of time is then used to calculate the distance between the sensor and the point the light reflects off. LiDAR sensors generate data points as they move across a field. Each point has an x, y, and z coordinate to give it a location in space. Through data analysis we can create 3-dimensional data and measure a physical feature such as plant height.

Selecting a Sensor for a Specific Trait

One of the benefits of using UAVs in plant breeding is the ability to mix and match sensors to fit your needs. While all sensors are being used and studied in agriculture, we will focus the rest of this lesson on implementing HTP into a breeding program to measure biomass. Biomass can be measured by a breeder with ‘hands on’ methods. Biomass, however, has a strong correlation with a sensor data generated value called the normalized difference vegetative index (NDVI). NDVI is one of the most commonly used vegetative indices. Vegetation indices can provide us with valuable information about the growth and stress crops are experiencing. They can also be used to differentiate between plant and non-plant material.

Two sensors a plant breeder would find useful for measuring biomass are RGB sensors and Multispectral sensors. The RGB camera does not help directly in measuring the biomass of plants, but it is used to create a picture of the entire field. The multispectral camera will capture data that can be used to calculate vegetation indices.