Remote Sensing for Crop Improvement: From Research to Industry

Maize is one the most widely cultivated crops worldwide (along with wheat and rice). They are important as they provide calories, particularly protein and sugar. With the human population increasing upward of 9 billion in the coming decades, maize production will need to increase to meet the higher demand. One obstacle to achieving this is the need to maintain productivity while also reducing the use of mineral nitrogen fertilizers and water consumption.

Plant breeding to meet these aims has proved successful in improving numerous crop characteristics, but existing results show that the speed of improvement needs to increase significantly. Breeding is a very organized and science-intensive process of product development, which requires the creation of tens of thousands of variable plants by crossing diverse parents, and then selecting the very few individuals that show improved performance for multiple criteria, during successive years. Thus, the process is costly and time-consuming, and any technology that can help predict the performance of each genotype while reducing the cost and amount of testing greatly improves the overall rate of genetic gain. We can predict the performance of untested individuals with genotyping data (provided that there is sufficient data on the performance of the calibration set of individuals). However, the plant breeding industry still struggles to acquire phenotype data on sufficient numbers of individuals, especially for complex traits.

We installed RGB (red, green, blue) cameras and multispectral reflectance spectrometers on a tractor, enabling us to screen the dynamic response of wheat varieties and small plants. Using this data, we showed that such systems can provide results relevant to the discovery of genes and their functions, and to screening for plant breeding as well, but the system was not adapted to taller plants as maize.

In response to this problem, we started with the development of an unmanned aerial platform that would be used to characterize the canopy structure of crop across the whole plant life cycle. The evolution of canopy structure of crops may provide efficient indications of the growing status, water stress and yield estimation. We identified aspects of the maize genome that coherently affect canopy reflectance, chlorophyll content, biomass, and yield. We showed that these measurements are sufficiently repeatable and some traits seems to be linked to the tolerance of such varieties to water deficiency.

We will discuss here the perspectives given by these remote sensing technologies in deciphering complex traits as a tool for breeders to characterize canopy function in thousands of plant varieties which requires throughput to be increased by one to two orders of magnitude and precision to be increased as well.