High-Throughput Phenotyping of Winter Wheat (Triticum aestivum L.) Survival and Plant Height
Proximal phenotyping of plant materials in the field takes immense time and resource commitments in breeding programs. Objectives of this research were to: 1) use spectral indices (SI) derived from unmanned aerial system (UAS) data to calculate a vegetation fraction used as a proxy for visual winter survival, 2) determine accuracy of SI integrated over-time in identifying high yielding wheat plots, 3) use UAS spectral data to build a digital surface model to estimate plant height. Yield trials were planted over two years with the agronomic traits winter survival, plant height, grain yield, and growth stage recorded. Our results indicated that survival could be accurately estimated using SI and unsupervised image classification, SI showed moderate ability to select the top yielding genotypes, and plant height can be accurately estimated from a single UAS flight. UAS derived phenotypic estimates of winter survival and plant height should replace manual measurements.