Estimation of vegetation indices for high-throughput phenotyping of wheat breeding lines using aerial imaging.

Background: Unmanned aerial vehicles ofer the opportunity for precision agriculture to efciently monitor agri‑
cultural land. A vegetation index (VI) derived from an aerially observed multispectral image (MSI) can quantify crop
health, moisture and nutrient content. However, due to the high cost of multispectral sensors, alternate, low-cost
solutions have lately received great interest. We present a novel method for model-based estimation of a VI using
RGB color images. The non-linear spatio-spectral relationship between the RGB image of vegetation and the index
computed by its corresponding MSI is learned through deep neural networks. The learned models can be used to
estimate VI of a crop segment.
Results: Analysis of images obtained in wheat breeding trials show that the aerially observed VI was highly corre‑
lated with ground-measured VI. In addition, VI estimates based on RGB images were highly correlated with VI deduced
from MSIs. Spatial, spectral and temporal information of images contributed to estimation of VI. Both intra-variety and
inter-variety diferences were preserved by estimated VI. However, VI estimates were reliable until just before signif‑
cant appearance of senescence.
Conclusion: The proposed approach validates that it is reasonable to accurately estimate VI using deep neural
networks. The results prove that RGB images contain sufcient information for VI estimation. It demonstrates that lowcost VI measurement is possible with standard RGB cameras.
Keywords: Wheat, Phenotyping, Deep learning, Precision agriculture