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