High Throughput Phenotyping Sensors

RGB sensors detect 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 captures numerous overlapping images throughout its flight, enabling researchers to generate an image of the entire field during the data processing stage. The final product will look similar to Fig. 8. The data captured by an RGB sensor can also be used to calculate vegetation indices. Vegetation indices can be used to understand plant health and biomass.

Multispectral sensors are designed to collect data outside the visible light spectrum. This includes wavelengths in the UV and NIR ranges. 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 calculate more vegetation indices than RGB sensors. The detection of these additional wavelengths provides a more sophisticated understanding of plant health and biomass.

Hyperspectral sensors capture a continuous range of wavelengths across the electromagnetic spectrum (Fig. 5). These sensors measure hundreds of wavelengths of light at once including those undetectable by the human eye. This information is useful when the specific wavelengths of light needed to measure a trait are unknown, making hyperspectral sensors good for exploratory research.

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 back to the sensor, as compared to most sensors used in HTP that sense the light reflected from the sun. 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 or area. 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. This type of data collection typically requires high-resolution global positioning system (GPS) data or 3D ground control points (GCP), or both, for reconstruction.

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.

One of the benefits of using UAVs in plant breeding is the ability to mix and match sensors to fit your needs. For example, RGB and LiDAR sensors can be useful for measuring physical traits like plant size, canopy cover, or height. If you want to look deeper into the plant to detect disease or pest infestations, thermal sensors would be more useful. If you are conducting exploratory research and not sure what sensor to use, the hyperspectral sensor will be useful because it can measure every wavelength allowing you to determine which wavelengths correlate with your trait of interest. When selecting a sensor, it is important to consider the traits of interest, technical expertise required, and budget.

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 Vegetation Index (NDVI). NDVI is one of the most commonly used vegetation 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 using vegetation indices are RGB sensors and Multispectral sensors. RGB sensors can be used to measure biomass but multispectral sensors provide a more accurate measure of biomass due to their sensitivity of the NIR wavelengths that can be used to calculate vegetation indices.