Multi-spectral Imaging in Winter Wheat Variety Improvement
Primary author: Andrew Herr
Faculty sponsor: Arron Carter
Primary college/unit: Agricultural, Human and Natural Resource Sciences
Multispectral imaging with unmanned aerial vehicles is a promising high-throughput phenotyping technology that has shown to help understand the causal mechanisms associated with crop productivity. This imaging technology can accurately predict complex agronomic traits like grain yield within a given generation, creating the potential to fast-track selections in plant breeding and increase genetic gains. Unfortunately, multispectral imaging has not been evaluated at selecting performance across years, limiting our understanding of predicting across environmental variation. The objective of this study is to determine the effectiveness and efficiency of prediction across years and locations within a breeding program. Spectral reflectance indices such as NDVI and NWI will be used to evaluate Washington State University winter wheat breeding lines between 2017 and 2020. Data will be collected using a DJI Phantom drone, equipped with a MicaSense camera, and data collected at heading date. Lines are observed from single location, single replication preliminary yield trials to multi-location, replicated advanced yield trials. Lines advanced in the breeding program will be evaluated across 20 different location-year trials. The indices collected from these trials will be used in indirect selection to estimate how well they predict performance of breeding lines across multiple location-years. Additionally, indices will be used as fixed effects in mixed models and genomic prediction modeling to further estimate their usefulness in genomie selection. The proposed research will be vital for plant breeder’s to understand the usefulness of multispectral imaging to improve winter wheat varieties while using fewer resources.