Non-Destructive Sampling for Monitoring the Growth and Performance of Winter Wheat Genotypes.

Wheat (Triticum aestivum L.) production can be enhanced through the development of improved cultivars with wider genetic base, capable of producing better yield under various agro-climatic conditions, biotic and abiotic stresses. Monitoring wheat performance during the growing season will provide information on productivity in terms of the yield potential. However, conventional methods are time-consuming, labor-intensive and can cause large sampling errors. Remote sensing tools have provided easy and quick measurements of leaf area index and aboveground biomass, without destructive sampling. The objective of this study is to evaluate genetic variability in growth and performance of wheat genotypes under two water regimes (rainfed and irrigated conditions), using spectral vegetation indices (SVI) estimated from aerial imagery, and from GreenSeeker® sensor, ground-based plant health sensing system. and percent ground cover (%GC) estimated from digital photos. Field experiments were conducted at the Texas A&M AgriLife Research Experiment Station in Bushland, Texas during the 2014-2016 winter wheat growing season. Data was collected and analyzed at three growth stages, after-emergence, tillering and late-jointing. Results showed that a significant variation exists among the genotypes for all the estimated parameters at early stages after emergence. Results showed that a significant variation exists among the genotypes. Significant relationships between the estimated parameters indicate that they could be used as an indirect selection tool for screening a large number of early-generation lines and advanced wheat genotypes. Overall, this study illustrated the potential use of remote sensing techniques by breeders for high-throughput phenotyping of wheat genotypes to screen for high-yielding and drought-tolerant genotypes.