Texas A&M Student Oral Presnters
Congratulations to our recipients:
Ph.D. Student, Plant Breeding
Texas A&M University
Bio: Ace Pugh is a doctoral candidate in Dr. Bill Rooney's sorghum breeding laboratory. His research focus is on the utilization of remote sensing techniques for high-throughput phenomics and the implementation of these technologies into a breeding program.
Temporal estimates of crop growth in sorghum and maize breeding enabled by unmanned aerial systems
Abstract: To meet future world food and fiber demands, plant breeders must increase the rate of genetic improvement of important agricultural crops. One of the biggest obstacles now facing crop scientists is a phenotyping bottleneck. To ease this burden, the emerging technology of unmanned aerial systems (UAS) presents an exciting opportunity. To assess the utility of UAS, it is important to investigate their application across multiple crop species. Terminal plant height is of great importance to maize [Zea mays] and sorghum [Sorghum bicolor L. Moench] breeders and temporal plant height has been hypothesized to be useful, but has been logistically impractical to measure in the field. In this study, we present an in-depth statistical analysis of the ability for UAS to estimate height in sorghum (Advanced and Early Generation material) and maize (Optimal and Stress material) and the applications of these estimates in breeding programs. We find that UAS explains genotypic variation similarly to ground-truth methods and that the repeatability of the methodology is high (R = 0.61 – 0.99), indicating effective differentiation of genotypes. Additionally, correlations between ground-truth and UAS measurements were moderate to high for all materials (r = 0.4 – 0.9). Finally, we present a novel application for the technology in the form of high-resolution temporal growth curves. Using these UAS-generated growth curves, new physiological insights can be obtained and new avenues of scientific investigation are possible.
Ph.D. Student, Molecular and Environmental Plant Sciences
Texas A&M University
Bio: Anna Casto is a 5th year Molecular and Environmental Plant Sciences PhD student in the Mullet Lab. Her research focuses on the genetics and physiology of sorghum stem development. She has previously served as the president of the Molecular and Environmental Plant Sciences Student Association and the chair of the annual MEPS symposium organizing committee.
SbNAC “D” developmentally regulates programmed cell death mediated aerenchyma formation in sorghum stems
Abstract:Meeting future energy demand requires efficient biomass feedstocks for biofuel production. Sorghum bicolor is a drought-resistant C4 grass that is a promising target for improvement as a biomass crop. Sorghum stems accumulate high concentrations of carbohydrates in their stems; however, air spaces or aerenchyma form in the stem parenchyma of certain varieties of sorghum, limiting the volume of sucrose that can accumulate in the stem and causing a dry stem phenotype. Aerenchyma first appear in pith cells of upper portion of elongating internodes of vegetatively growing sorghum, first in the center of the stem then expanding downward and radially outward. The “D” gene was described over 100 years ago by Hilson (1916) as a major gene influencing the “juiciness” of sorghum stems and leaf midribs. Through quantitative trait locus (QTL) mapping in three biparental populations (BTx623*IS320c, BTx623*R07007, BTx623*Standard broomcorn), we identified a locus on chromosome 6 that explains a large amount of variation in aerenchyma formation. We fine mapped the QTL to a gene encoding a NAC (NAM, ATAF1/2, and CUC2) family transcription factor. A single nucleotide polymorphism (SNP) causes a stop codon in the first exon of the gene in BTx623. SbNAC_D is induced in stem segments that are positioned to form aerenchyma. Transcriptome analysis of stem tissue from SbNAC_DD and SbNAC_dd genotypes revealed programmed cell death genes were up-regulated. We propose that allelic variation in the SbNAC_D modulates the extent of developmentally regulated programmed cell death resulting in dry or juicy stems.
Ph.D. Student, Agronomy
Texas A&M University
Bio: Mahendra is currently a second year Ph.D student, from Nepal. He is working under Dr. Amir Ibrahim and Dr. Qingwu Xue in wheat breeding and crop stress program. He received his bachelor’s degree in Agriculture science from Tribhuvan University, Nepal. He moved to US at West Texas A&M University and did his Masters in Plant, Soil and Environmental Science. His current research is on the use of high- throughput phenotyping system in wheat breeding, with a focus on disease, yield and water stress phenotyping. He is interested in integrating plant breeding, agronomy and remote sensing for developing better techniques to study the physiological and genotypic variability in plants.
High Throughput Phenotyping of Foliar Disease Resistance in Wheat
Abstract: Remote sensing has been widely used as an indirect approach to study the agronomic and physiological traits of plants. Plant diseases cause significant yield reductions in wheat (Triticum aestivum L.). Remote detection and assessment of plant diseases are important to improve disease phenotyping in breeding programs. This study investigates the potential use of low-cost Unmanned Aerial System (UAS), equipped with RGB and multispectral sensors, to quantify leaf rust severity caused by the fungus Puccinia Triticina in wheat. In addition, Green seeker was used to obtain Normalized Difference Vegetation Index (NDVI) measurements. RGB images were acquired using rotary wing (DJI Phantom 4 pro) from the field of different wheat genotypes grown at Castroville, TX. Different vegetation indices (VIs) and image classification approaches were applied to separate the diseased and healthy canopies. The obtained image dataset was further processed to generate plot level data. The relationship between VIs and visual field data on disease severity was examined to find a suitable vegetation index that can evaluate the leaf rust severity in wheat. Disease severity was highly correlated to excess green index (r= -0.86), NDVI (r= -0.91, P<0.01), and Band Ratio (r=0.89, P<0.01). The results show that UAS imaging and automated data extraction can help to obtain high throughput phenotyping data on disease severity with higher precision. This tool has a great potential to enable rapid assessment of the large breeding nurseries by providing high-resolution measurements from small plots and observation rows.