It may be a few years before opti-electronic sensors manage individual canola plants, but scientists are already using this new technology in plant breeding programs.
Texas A&M is developing sorghum varieties for bioenergy production. They’re selecting for a long list of characteristics, including yield, drought tolerance, plant height, leaf size, nitrogen efficiency, time to maturity and minimal input levels.
Measuring and logging this data is known as phenotyping, typically a time-consuming endeavour based mainly on human observation, which is not always perfect.
Like most plant breeding programs, the selection starts with tens of thousands of specimens and gradually narrows down as the criteria becomes tighter over time, according to Texas A&M researcher Alex Thomasson, the lead scientist charged with developing opti-electronic sensors at the university.
“A major limitation in the genetic improvement of energy crops is the collection of large, good quality phenotypic data. Traditional plant phenotypic measurements rely on humans and are slow, expensive and subjective,” said Thomasson.
“(Opti-electronic) will enable the measurement of plants along their full growth cycle, allowing the traits such as speed and form of growth, flowering and final biomass yield/quality to be investigated.”
Five of the sensors he has brought to the forefront will help the team develop a phenotyping system for energy sorghum that focuses mainly on yield, drought tolerance and nitrogen use efficiency.
- The down-looking six-band, multi-spectral camera used to assess nitrogen content, growth status and plant size.
- The down-looking thermal imaging camera that measures plant canopy temperature and water content.
- The light curtain which measures plant height, projected plant profile and plant size.
- The side-looking camera that gives a plant profile view.
- The ultrasonic sensor which gives another measurement of plant height.
“The redundancy is desirable because some sensors perform better in greenhouses, while others are more suitable for field applications.
“Having a complementary set of devices and techniques for plant measurements will enable us to have different systems suited to specific environments,” he said.
“Other indicators of plant performance can be derived from a combination of measurements from the group of sensors.
Combining projected leaf area with plant height can be a good indication of plant size and amount of biomass.
Combining the down-looking and side-looking images provides the opportunity for the 3D reconstruction of the plants.
“Another advantage of the automated sensor approach is that readings on a large number of plants can be collected weekly or even daily at a high level of accuracy, a process that would not ordinarily be economically feasible using human workers.”
The in-field, plant-by-plant applicator project doesn’t involve data storage or manipulation. It’s simply sense and squirt. However, the phenotyping plant breeding project is a much bigger challenge.
There needs to be a software program to control and co-ordinate the sensors. Robust image-processing algorithms are required to distinguish sorghum plants from the background.
Finally, a specialized program has to store sensor output in a relational database.
“There has been some sensor-based phenotyping research done in the past on plants, but a turnkey system doesn’t exist. My goal is always to try to get the technology to a commercialization phase and I think this has potential,” says Thomasson.
“At this point, we want to demonstrate that our platform can provide rapid cost-effective ranking and screening of hundreds of candidate lines for the desired traits and eventually lead to a more efficient breeding program.”