Hyperspectral phenotyping has the potential to act as a surrogate for standard reference measurements of physiological and anatomical leaf traits related with w

Whole genome sequencing has allowed rapid progress in the application of forward genetics in model species. In this study, we demonstrated an application of next-generation sequencing for forward genetics in a complex crop genome. We sequenced an ethyl methanesulfonate-induced mutant of Sorghum bicolor defective in hydrogen cyanide release and identified the causal mutation. A workflow identified the causal polymorphism relative to the reference BTx623 genome by integrating data from single nucleotide polymorphism identification, prior information about candidate gene(s) implicated in cyanogenesis, mutation spectra, and polymorphisms likely to affect phenotypic changes. A point mutation resulting in a premature stop codon in the coding sequence of dhurrinase2, which encodes a protein involved in the dhurrin catabolic pathway, was responsible for the acyanogenic phenotype. Cyanogenic glucosides are not cyanogenic compounds but their cyanohydrins derivatives do release cyanide. The mutant accumulated the glucoside, dhurrin, but failed to efficiently release cyanide upon tissue disruption. Thus, we tested the effects of cyanide release on insect herbivory in a genetic background in which accumulation of cyanogenic glucoside is unchanged. Insect preference choice experiments and herbivory measurements demonstrate a deterrent effect of cyanide release capacity, even in the presence of wild-type levels of cyanogenic glucoside accumulation. Our gene cloning method substantiates the value of (1) a sequenced genome, (2) a strongly penetrant and easily measurable phenotype, and (3) a workflow to pinpoint a causal mutation in crop genomes and accelerate in the discovery of gene function in the postgenomic era.

Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R² values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

Sorghum has been grown as a food crop for many centuries in Africa and India. Food-grade sorghum is becoming an increasingly important crop in the developed world, especially as a cereal grain option for people with celiac disease. The highest quality sorghum flours and food products are produced using grain from food-grade sorghum varieties. Food-grade sorghum varieties differ from conventional varieties in several key traits. The objective of this review article is to describe and summarize these differences and important considerations for food-grade sorghum production.