Sorghum High-throughput Phenotyping Platform for Greenhouses

Plant phenotyping involves collecting information on the physical characteristics of plants. The information collected assists breeders and biologists to improve desired traits in crops. It is crucial to understand the behavior of crop plants in controlled settings so that genetic differences can be observed. In this period of increasing energy demand, renewable and carbon-neutral energy sources have become the subject of more research. One crop that is a possible biomass-energy source is energy sorghum, which does not compete as food source and is efficient at accumulating biomass. The stalk-thickness and height of energy sorghum are the main phenotypic parameters of interest, because 70-80 percent of the biomass is stored in the stalk. Measuring the stalk of energy sorghum can enable estimation of biomass yield. However, a phenotyping system dedicated to high-throughput data collection in energy sorghum in a greenhouse has yet to be developed. The research presented herein details the design, construction and testing of a semi-automated phenotyping system for energy sorghum plants in a greenhouse. Image collection, processing and analysis are evaluated as a potential method for measuring plant stalk thickness. The system proved capable of collecting digital images of 288 energy sorghum plants – a representative number for the greenhouse in the study – in 10.5 hours. Images were collected with 75% overlap and were stitched together manually with the GIMP software package to obtain a complete image of an individual plant. K-means segmentation was used to separate plant matter from background in the images, and a stalk-measurement algorithm was developed. Results of these image analysis techniques provided an average of 16% error as compared to measurements obtained with a caliper. The results of this research suggest that this phenotyping method is viable, with high-throughput and mainly limited by image stitching

High Throughput Phenotyping of Sorghum for the Study of Growth Rate, Water Use Efficiency, and Chemical Composition

Plant phenotyping using digital images has increased the throughput of the trait measurement process, and it is considered to be a potential solution to the problem of the phenotyping bottleneck. In this study, RGB images were used to study relative growth rate (RGR) and water use efficiency (WUE) of a diverse panel of 300 sorghum plants of 30 genotypes, and hyperspectral images were used for chemical analysis of macronutrients and cell wall composition. Half of the plants from each genotype were subjected to drought stress, while the other half were left unstressed. Quadratic models were used to estimate the shoot fresh and dry weights from plant projected area. RGR values for the drought-stressed plants were found to gradually lag behind the values for the unstressed plants. WUE values were highly variable with time. Significant effects of drought stress and genotype were observed for both RGR and WUE. Hyperspectral image data (546 nm to 1700 nm) were used for chemical analysis of macronutrients (N, P, and K), neutral detergent fiber (NDF), and acid detergent fiber (ADF) for plant samples separated into leaf and three longitudinal sections of the stem. The accuracy of the models built using the spectrometer data (350 nm to 2500 nm) of dried and ground biomass was found to be higher than the accuracy of models built using the image data. For the image data, the models for N(R2 = 0.66, RPD = 1.72), and P(R2=0.52, RPD = 1.46) were found to be satisfactory for quantitative analysis whereas the models for K, NDF, and ADF were not suitable for quantitative prediction. Models built after the separation of leaf and stem samples showed variation in the accuracy between the two groups. This study indicates that image-based non-destructive analysis of plant growth rate and water use efficiency can be used for studying and comparing the effects of drought across multiple genotypes. It also indicates that two dimensional hyperspectral imaging can be a useful tool for non-destructive analysis of chemical content at the tissue level, and potentially at the pixel level. Advisor: Yufeng G

QTL Mapping Of Stem Biomechanical Traits and Precision Phenotyping in Bioenergy Sorghum

Mechanical characterization is an important and now frequently used tool for phenotyping plants for crop improvement, e.g. lodging resistance. Mechanics of materials and structures in response to various external stimuli as well as information of basic building blocks that constitute the plants can be applied to study the mechanical behavior of plant stems. The inherent mechanical properties of plant structures such as the stem are relevant to breeding strategies, aiming to tackle issues such as crop lodging due to stem or root lodging. While empirical tests of breaking strength and stiffness have been applied to plants, few of these studies consider the genetic background of the plants examined. In this study, we report for the first time on the mapping of QTL for mechanical traits in sorghum in three RIL mapping populations from crosses between grain and sweet sorghum parents. The genetic architecture of biomechanical traits in the three RIL populations appear to be quantitative and pleiotropic. Six QTL affecting mechanical and morphological traits were detected; two of these QTL were consistently found in all populations and co-localized with previously cloned dwarfing genes Dw1 and Dw3. These results suggest that dwarfing genes affect the mechanical properties of sorghum and ultimately their lodging resistance while also having a profound impact on the stem’s morphology and geometry. Morpho-anatomical stem properties are major component affecting standability. However, phenotyping these traits is low throughput, and has been restricted by the lack of a high-throughput phenotyping platform that can collect both morphological and anatomical stem properties. X-ray computed tomography (CT) offers a potential solution, but studies using this technology in plants have evaluated limited numbers of genotypes. The platform and image analysis pipeline revealed extensive phenotypic variation for important morpho-anatomical traits in well-characterized sorghum genotypes at suitable repeatability rates. CT estimates were highly predictive of morphological traits and moderately predictive of anatomical traits. The image analysis pipeline also identified genotypes with superior morpho-anatomical traits that there were consistent with ground-truth based classification in previous studies. In addition, stem cross section intensity measured by the CT was highly correlated with stem dry weight density, and can potentially serve as a high-throughput approach to measure stem density in grasses

Sensors in Agriculture: Systems and Methods for Two Sensor Systems for Plant Phenotype Detection

Sensors are increasingly being used in agricultural settings to provide data on the physical characteristics of plants under field conditions. Accurate data provides researchers and producers with the ability to make decisions with a high level of confidence. This work addresses two sensing systems for measuring important plant characteristics. The first system investigates accuracy differences between two unmanned aerial vehicle (UAV) camera calibration methods. The second system explores the development and testing of a novel in situ root imaging rhizotron. The UAV study compared autoexposure and fixed exposure radiometric calibration methods to a single calibrated manned aircraft image and to a ground target measured with a spectroradiometer. In a band by band comparison, the autoexposure method, which uses a pre-flight image of a single panel for calibration, produced almost twice as much radiometric error on average compared with fixed exposure using in-field targets for image calibration. When comparing the exposure methods using the Visible Atmospherically Resistant Index (VARI), the autoexposure method produced twice as much RMSE compared to the fixed exposure method. The study on the novel in situ root sensor developed a low field magnetic resonance imaging (LF-MRI) rhizotron. A scaled 8 cm bore model was designed, built and test across three types of soil, Weswood silt loam, Belk clay, and Houston black clay. The results demonstrated the viability of this technology to produce root information in clay soils. A 28 cm bore unit was designed, built and tested under field conditions. The resulting system provided root information and visualization of roots with 2-D projection images in a Weswood silt loam, and Belk clay both in situ and ex situ. In summary, (1) using a fixed exposure calibration method for UAV remote sensing improved accuracy in reflectance data, providing a better understanding of in-field plant conditions and better decision-making capability; and (2) the LF-MRI Rhizotron allowed visualization of plant roots in agricultural soils under field conditions. Both sensing systems and methods have the potential to be used as tools for improving crop production for researchers or growers

Understanding the physiological and molecular aspects of charcoal rot resistance mechanisms in sorghum and soybean

Doctor of PhilosophyDepartment of Plant PathologyChristopher R LittleCharcoal rot (CR) of soybean (Glycine max (L.) Merr.) and sorghum (Sorghum bicolor (L.) Moench) is caused by the hemibiotrophic soilborne fungus Macrophomina phaseolina (MP) and is an important pathogen in the midwestern United States. Complex molecular mechanisms underlie the interaction of MP with these two hosts, which impedes resistance breeding. To select for charcoal rot resistance, a thorough understanding of the host’s physiological and molecular responses to MP along with screening of genotypes with resistance to CR is essential. To understand MP induced host’s physiological and molecular responses, first we investigated MP-induced oxidative stress-mediated senescence by using the reactive oxygen species (ROS) scavenger ascorbic acid in soybean seedlings. Three soybean isolates of MP were tested for their sensitivity to ascorbic acid using an in-vitro assay. An in-planta soybean cut-stem assay was used for the exogenous application of ascorbic acid (oxidized and reduced form) following inoculation with MP. A ROS (H2O2) quantification assay was used to validate H2O2 induced by MP and ascorbic acid pre-treatment. All three MP isolates were sensitive to ascorbic acid concentrations of ≥ 15 mM. Ascorbic acid (10mM) pre-treatment following MP inoculation reduced CR lesion length compared to inoculated treatment. MP induced a significantly higher H2O2 than ascorbic acid pre-treated inoculated plant. Second, through comparative transcriptomics, MP-resistant and susceptible soybean genotypes revealed contrasted responses to MP-induced senescence. Gene Ontology and pathway analysis showed MP-induced receptor kinase like genes in both genotypes while down-regulated defense related antioxidant, hormonal, and other metabolic pathways in both genotypes. Ascorbic acid pre-treatment induced a more significant number of photosynthesis genes in both genotypes. Hydrogen peroxide pre-treatment following inoculation showed up-regulation of oxidative stress responsive pathways while down-regulated photosynthesis and hormonal signal transduction pathways. Third, the NAM phenotyping for CR resistance results of location- and year-wise data showed strong genotype by environment interactions. Overall, using MP screening, charcoal rot resistance phenotyping in the NAM parental lines revealed the genotype SC1103 as the most resistant line and Segaolane and Macia as the most susceptible. The SC1103 NAM family-derived population can be used for charcoal rot resistance in association studies to map charcoal rot resistance

Imaging for High-Throughput Phenotyping in Energy Sorghum

The increasing energy demand in recent years has resulted in a continuous growing interest in renewable energy sources, such as efficient and high-yielding energy crops. Energy sorghum is a crop that has shown great potential in this area, but needs further improvement. Plant phenotyping—measuring physiological characteristics of plants—is a laborious and time-consuming task, but it is essential for crop breeders as they attempt to improve a crop. The development of high-throughput phenotyping (HTP)—the use of autonomous sensing systems to rapidly measure plant characteristics—offers great potential for vastly expanding the number of types of a given crop plant surveyed. HTP can thus enable much more rapid progress in crop improvement through the inclusion of more genetic variability. For energy sorghum, stalk thickness is a critically important phenotype, as the stalk contains most of the biomass. Imaging is an excellent candidate for certain phenotypic measurements, as it can simulate visual observations. The aim of this study was to evaluate image analysis techniques involving K-means clustering and minimum-distance classification for use on red-green-blue (RGB) images of sorghum plants as a means to measure stalk thickness. Additionally, a depth camera integrated with the RGB camera was tested for the accuracy of distance measurements between camera and plant. Eight plants were imaged on six dates through the growing season, and image segmentation, classification and stalk thickness measurement were performed. While accuracy levels with both image analysis techniques needed improvement, both showed promise as tools for HTP in sorghum. The average error for K-means with supervised stalk measurement was 10.7% after removal of known outliers