Maize Phenotypes Research Articles

Genomic and Transcriptomic Analyses Reveal Pathways and Genes Associated With Brittle Stalk Phenotype in Maize.

The mechanical strength of the stalk affects the lodging resistance and digestibility of the stalk in maize. The molecular mechanisms regulating the brittleness of stalks in maize remain undefined. In this study, we constructed the maize brittle stalk mutant (bk5) by crossing the W22:Mu line with the Zheng 58 line. The brittle phenotype of the mutant bk5 existed in all of the plant organs after the five-leaf stage. Compared to wild-type (WT) plants, the sclerenchyma cells of bk5 stalks had a looser cell arrangement and thinner cell wall. Determination of cell wall composition showed that obvious differences in cellulose content, lignin content, starch content, and total soluble sugar were found between bk5 and WT stalks. Furthermore, we identified 226 differentially expressed genes (DEGs), with 164 genes significantly upregulated and 62 genes significantly downregulated in RNA-seq analysis. Some pathways related to cellulose and lignin synthesis, such as endocytosis and glycosylphosphatidylinositol (GPI)-anchored biosynthesis, were identified by the Kyoto Encyclopedia of Gene and Genomes (KEGG) and gene ontology (GO) analysis. In bulked-segregant sequence analysis (BSA-seq), we detected 2,931,692 high-quality Single Nucleotide Polymorphisms (SNPs) and identified five overlapped regions (11.2 Mb) containing 17 candidate genes with missense mutations or premature termination codons using the SNP-index methods. Some genes were involved in the cellulose synthesis-related genes such as ENTH/ANTH/VHS superfamily protein gene (endocytosis-related gene) and the lignin synthesis-related genes such as the cytochrome p450 gene. Some of these candidate genes identified from BSA-seq also existed with differential expression in RNA-seq analysis. These findings increase our understanding of the molecular mechanisms regulating the brittle stalk phenotype in maize.

Modulation of the DA1 pathway in maize shows that translatability of information from Arabidopsis to crops is complex

Modern agriculture is struggling to meet the increasing food, silage and raw material demands due to the rapid growth of population and climate change. In Arabidopsis, DA1 and DAR1 are proteases that negatively regulate cell proliferation and control organ size. DA1 and DAR1 are activated by ubiquitination catalyzed by the E3 ligase BIG BROTHER (BB). Here, we characterized the DA1, DAR1 and BB gene families in maize and analyzed whether perturbation of these genes regulates organ size similar to what was observed in Arabidopsis. We generated da1_dar1a_dar1b triple CRISPR maize mutants and bb1_bb2 double mutants. Detailed phenotypic analysis showed that the size of leaf, stem, cob, and seed was not consistently enlarged in these mutants. Also overexpression of a dominant-negative DA1R333K allele, resembling the da1-1 allele of Arabidopsis which has larger leaves and seeds, did not alter the maize phenotype. The mild negative effects on plant height of the DA1R333K_bb1_bb2 mutant indicate that the genes in the DA1 pathway may control organ size in maize, albeit less obvious than in Arabidopsis.

CHROMOMETHYLTRANSFERASE3/KRYPTONITE maintains the sulfurea paramutation in Solanum lycopersicum

SignificanceParamutation involves the transfer of a repressive epigenetic mark between silent and active alleles. It is best known from exceptional non-Mendelian inheritance of conspicuous phenotypes in maize but also in other plants and animals. Recent genomic studies, however, indicate that paramutation may be less exceptional. It may be a consequence of wide-cross hybridization and may contribute to quantitative trait variation or unstable phenotypes in crops. Using the sulfurea (sulf) locus in tomato, we demonstrate that a self-reinforcing feedback loop involving DNA- and histone-methyl transferases CHROMOMETHYLTRANSFERASE3 (CMT3) and KRYPTONITE (KYP) is required for paramutation of sulf and that there is a change in chromatin organization. These findings advance the understanding of non-Mendelian inheritance in plants.

Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks

High-throughput maize phenotyping at both organ and plant levels plays a key role in molecular breeding for increasing crop yields. Although the rapid development of light detection and ranging (LiDAR) provides a new way to characterize three-dimensional (3D) plant structure, there is a need to develop robust algorithms for extracting 3D phenotypic traits from LiDAR data to assist in gene identification and selection. Accurate 3D phenotyping in field environments remains challenging, owing to difficulties in segmentation of organs and individual plants in field terrestrial LiDAR data. We describe a two-stage method that combines both convolutional neural networks (CNNs) and morphological characteristics to segment stems and leaves of individual maize plants in field environments. It initially extracts stem points using the PointCNN model and obtains stem instances by fitting 3D cylinders to the points. It then segments the field LiDAR point cloud into individual plants using local point densities and 3D morphological structures of maize plants. The method was tested using 40 samples from field observations and showed high accuracy in the segmentation of both organs (F-score =0.8207) and plants (F-score =0.9909). The effectiveness of terrestrial LiDAR for phenotyping at organ (including leaf area and stem position) and individual plant (including individual height and crown width) levels in field environments was evaluated. The accuracies of derived stem position (position error =0.0141 m), plant height (R2 >0.99), crown width (R2 >0.90), and leaf area (R2 >0.85) allow investigating plant structural and functional phenotypes in a high-throughput way. This CNN-based solution overcomes the major challenges in organ-level phenotypic trait extraction associated with the organ segmentation, and potentially contributes to studies of plant phenomics and precision agriculture.

PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping.

A low-cost portable wild phenotyping system is useful for breeders to obtain detailed phenotypic characterization to identify promising wild species. However, compared with the larger, faster, and more advanced in-laboratory phenotyping systems developed in recent years, the progress for smaller phenotyping systems, which provide fast deployment and potential for wide usage in rural and wild areas, is quite limited. In this study, we developed a portable whole-plant on-device phenotyping smartphone application running on Android that can measure up to 45 traits, including 15 plant traits, 25 leaf traits and 5 stem traits, based on images. To avoid the influence of outdoor environments, we trained a DeepLabV3+ model for segmentation. In addition, an angle calibration algorithm was also designed to reduce the error introduced by the different imaging angles. The average execution time for the analysis of a 20-million-pixel image is within 2,500 ms. The application is a portable on-device fast phenotyping platform providing methods for real-time trait measurement, which will facilitate maize phenotyping in field and benefit crop breeding in future.

Genome assembly of the maize inbred line A188 provides a new reference genome for functional genomics

The current assembled maize genomes cannot represent the broad genetic diversity of maize germplasms. Acquiring more genome sequences is critical for constructing a pan-genome and elucidating the linkage between genotype and phenotype in maize. Here we describe the genome sequence and annotation of A188, a maize inbred line with high phenotypic variation relative to other lines, acquired by single-molecule sequencing and optical genome mapping. We assembled a 2210-Mb genome with a scaffold N50 size of 11.61 million bases (Mb), compared to 9.73 Mb for B73 and 10.2 Mb for Mo17. Based on the B73_RefGen_V4 genome, 295 scaffolds (2084.35 Mb, 94.30% of the final genome assembly) were anchored and oriented on ten chromosomes. Comparative analysis revealed that ~30% of the predicted A188 genes showed large structural divergence from B73, Mo17, and W22 genomes, which causes high protein divergence and may lead to phenotypic variation among the four inbred lines. As a line with high embryonic callus (EC) induction capacity, A188 provides a convenient tool for elucidating the molecular mechanism underlying the formation of EC in maize. Combining our new A188 genome with previously reported QTL and RNA sequencing data revealed eight genes with large structural variation and two differentially expressed genes playing potential roles in maize EC induction.

Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping

Unmanned aerial vehicle (UAV) system is an emerging remote sensing tool for profiling crop phenotypic characteristics, as it distinctly captures crop real-time information on field scales. For optimizing UAV agro-monitoring schemes, this study investigated the performance of single-source and multi-source UAV data on maize phenotyping (leaf area index, above-ground biomass, crop height, leaf chlorophyll concentration, and plant moisture content). Four UAV systems [i.e., hyperspectral, thermal, RGB, and Light Detection and Ranging (LiDAR)] were used to conduct flight missions above two long-term experimental fields involving multi-level treatments of fertilization and irrigation. For reducing the effects of algorithm characteristics on maize parameter estimation and ensuring the reliability of estimates, multi-variable linear regression, backpropagation neural network, random forest, and support vector machine were used for modeling. Highly correlated UAV variables were filtered, and optimal UAV inputs were determined using a recursive feature elimination procedure. Major conclusions are (1) for single-source UAV data, LiDAR and RGB texture were suitable for leaf area index, above-ground biomass, and crop height estimation; hyperspectral outperformed on leaf chlorophyll concentration estimation; thermal worked for plant moisture content estimation; (2) model performance was slightly boosted via the fusion of multi-source UAV datasets regarding leaf area index, above-ground biomass, and crop height estimation, while single-source thermal and hyperspectral data outperformed multi-source data for the estimation of plant moisture and leaf chlorophyll concentration, respectively; (3) the optimal UAV scheme for leaf area index, above-ground biomass, and crop height estimation was LiDAR + RGB + hyperspectral, while considering practical agro-applications, optical Structure from Motion + customer-defined multispectral system was recommended owing to its cost-effectiveness. This study contributes to the optimization of UAV agro-monitoring schemes designed for field-scale crop phenotyping and further extends the applications of UAV technologies in precision agriculture.

Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

BackgroundMaize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets.ResultsOn the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625.ConclusionThe Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

Structural features of five types of maize starch granule subgroups sorted by flow cytometry

Subgroups of starch granules from five maize phenotypes including waxy-, normal-, popcorn-, sweet corn- and high-amylose maize were sorted by flow cytometry (FC) utilizing the side scatter channel (SSC) and forward scatter channel (FSC). SSC and FSC mainly reflecting internal object complexity, and object size, respectively. Subgroups with higher FSC signal always showed higher SSC signal, indicating larger granules exhibited higher internal structural complexity. Wide-angle and small-angle X-ray scattering analysis showed that the subgroups showing high SSC signal intensity also had high lamellar scattering intensity, and low crystallinity. Vibrational transitions of bonds analyzed by Fourier Transform Infrared Spectroscopy (FT-IR) showed that the subgroups of maize starches, except sweet corn starch, with high SSC signal had high intensities at 1045 and 1022 cm−1. Hence, our data demonstrate that the structural complexity detected by the SSC signal is mainly associated with lamellar and crystalline features of starch granules.

Estimating the contribution of plant traits to light partitioning in simultaneous maize/soybean intercropping.

Spatial configuration and plant phenotypic plasticity contribute to increased light capture in relay intercropping, but there is little information on whether these factors also increase light capture in simultaneous intercropping. We developed and validated a three-dimensional functional-structural plant model to simulate light capture in maize and soybean sole crops and intercrop scenarios, using species traits observed in sole crops and intercrops. The intercrop maize phenotype had 2% greater light capture than the sole crop phenotype in a pure stand. The soybean intercrop phenotype had 5-10% lower light capture than the sole crop phenotype in a pure stand. The intercrop configuration increased the light capture of maize by 29% and reduced the light capture of soybean by 42%, compared with the light capture expected from sole crops. However, intercrop configuration only marginally affected total light capture by the intercrop system (+1%). Testing of individual soybean plant traits revealed that plasticity in leaf dimensions was the main reason for differences in light capture by soybean in simulated sole crops and intercrops. The results of this study illustrate a major shift of light capture from shorter species (soybean) to the taller component (maize) in a simultaneous strip intercrop. Plastic plant traits modulate this overall effect, but only marginally.

Identification and fine mapping of a recessive gene controlling zebra leaf phenotype in maize.

The online version contains supplementary material available at 10.1007/s11032-021-01202-7.

Identification and fine mapping of a candidate gene for oil yellow leaf 2 conferring yellow leaf phenotype in maize

AbstractLeaf colour is an important agronomic trait for studying molecular mechanisms in chlorophyll biosynthesis and chloroplast development. Here, a novel mutant oil yellow leaf 2 (Oy2) with a typical yellow leaf phenotype at the seedling stage was identified from the mutant population derived from the maize inbred line RP125. Compared with wild type, Oy2 mutant displays decreased chlorophyll content, reduced photosynthetic capacity and impaired chloroplast structure, which is likely controlled by a single recessive gene. The Oy2 locus was then delimited into a 117 kb region on chromosome 5 harbouring four genes, amongst which the gene Zm00001d013013, encoding a magnesium chelatase subunit D, was identified as the only candidate gene associated with Oy2 mutant phenotype. Moreover, the expression levels of candidate gene Oy2 and genes associated with chlorophyll biosynthesis and photosynthesis were tested by RNA‐seq and qRT‐PCR, implying that the causal gene Oy2 playing a critical role in chlorophyll synthesis. Taken together, we propose that the causal gene Oy2 highly associated with the yellow leaf phenotype may be helpful in elucidating photosynthetic pigments biosynthesis and chloroplast development in maize.

The maize premature senesence2 encodes for PHYTOCHROME-DEPENDENT LATE-FLOWERING and its expression modulation improves agronomic traits under abiotic stresses.

Among the various abiotic stresses, water and nitrogen are major stress factors that limit crop productivity worldwide. Since leaf nutrients remobilization during leaf senescence might impact response to abiotic stress in crops, we undertook a forward screen of the Mutator‐active approach to identify premature senescence loci in maize. A mutant line isolated from a cross between a Pioneer Brand elite line and a public Mutator‐active material, designated premature senescence2 (pre2), expressed leaf senescence during flower initiation. The Pre2 gene encodes PHYTOCHROME‐DEPENDENT LATE‐FLOWERING (PHL) protein, a nuclear receptor coactivator. The pre2‐1 mutant allele was not a null mutation but produced a functional wild‐type transcript along with multiple mRNA species of varying lengths resulting from the alternate splicing of the Pre2 gene. The PHL accelerates flowering by suppressing the inhibitory effect of phyB on flowering in Arabidopsis (Endo et al., 2013). The ZmPRE2 polypeptide is highly conserved in plant species and has two identifiable motifs namely SPT20 and MED15. The Spt20 domain, which is a part of the SAGA (Spt‐Ada‐Gcn5 acetyltransferase) complex, is involved in histone deacetylation and MED15 proteins have nuclear functions in mediating DNA Pol II transcription. The differential spliced mature transcripts in both the pre2 alleles, as a result of transposon interference, were producing truncated proteins that lacked polyglutamine (Q) tract near the C‐terminus and might be causative of the premature senescence phenotype in maize. Endogenous gene suppression of ZmPre2 by RNAi improves maize agronomic performance under both water stress and suboptimal nitrogen conditions. The homozygous T‐DNA knockout of the pre2 homolog in Arabidopsis (At1G72390; the same insertional allele used by Endo et al., 2013) results in higher biomass, delayed maturity, enhanced tolerance to drought, and improved nitrogen utilization efficiency. The Arabidopsis mutant also showed hypersensitive response to 1 µM ABA (abscisic acid) concentration. These results indicate that the PHL protein plays a direct or indirect role in ABA‐dependent drought and N signaling pathways.

Endogenous Hormones Inhibit Differentiation of Young Ears in Maize (Zea mays L.) Under Heat Stress.

Global warming frequently leads to extreme temperatures, which pose a serious threat to the growth, development, and yield formation of crops such as maize. This study aimed to deeply explore the molecular mechanisms of young ear development under heat stress. We selected the heat-tolerant maize variety Zhengdan 958 (T) and heat-sensitive maize variety Xianyu 335 (S), and subjected them to heat stress in the V9 (9th leaf), V12 (12th leaf), and VT (tasseling) growth stages. We combined analysis of the maize phenotype with omics technology and physiological indicators to compare the differences in young ear morphology, total number of florets, floret fertilization rate, grain abortion rate, number of grains, and main metabolic pathways between plants subjected to heat stress and those left to develop normally. The results showed that after heat stress, the length and diameter of young ears, total number of florets, floret fertilization rate, and number of grains all decreased significantly, whereas the length of the undeveloped part at the top of the ear and grain abortion rate increased significantly. In addition, the differentially expressed genes (DEGs) in young ears were significantly enriched in the hormone signaling pathways. The endogenous hormone content in young ears exhibited different changes: zeatin (ZT) and zeatin riboside (ZR) decreased significantly, but gibberellin acid3 (GA3), gibberellin acid4 (GA4), and abscisic acid (ABA) increased significantly, in ears subjected to heat stress. In the heat-tolerant maize variety, the salicylic acid (SA), and jasmonic acid (JA) content in the vegetative growth stage also increased in ears subjected to heat stress, whereas the opposite effect was observed for the heat-sensitive variety. The changes in endogenous hormone content of young ears that were subjected to heat stress significantly affected ear development, resulting in a reduction in the number of differentiated florets, fertilized florets and grains, which ultimately reduced the maize yield.

Use of virus-induced gene silencing to characterize genes involved in modulating hypersensitive cell death in maize.

Plant disease resistance proteins (R‐proteins) detect specific pathogen‐derived molecules, triggering a defence response often including a rapid localized cell death at the point of pathogen penetration called the hypersensitive response (HR). The maize Rp1‐D21 gene encodes a protein that triggers a spontaneous HR causing spots on leaves in the absence of any pathogen. Previously, we used fine mapping and functional analysis in a Nicotiana benthamiana transient expression system to identify and characterize a number of genes associated with variation in Rp1‐D21‐induced HR. Here we describe a system for characterizing genes mediating HR, using virus‐induced gene silencing (VIGS) in a maize line carrying Rp1‐D21. We assess the roles of 12 candidate genes. Three of these genes, SGT1, RAR1, and HSP90, are required for HR induced by a number of R‐proteins across several plant–pathogen systems. We confirmed that maize HSP90 was required for full Rp1‐D21‐induced HR. However, suppression of SGT1 expression unexpectedly increased the severity of Rp1‐D21‐induced HR while suppression of RAR1 expression had no measurable effect. We confirmed the effects on HR of two genes we had previously validated in the N. benthamiana system, hydroxycinnamoyltransferase and caffeoyl CoA O‐methyltransferase. We further showed the suppression the expression of two previously uncharacterized, candidate genes, IQ calmodulin binding protein (IQM3) and vacuolar protein sorting protein 37, suppressed Rp1‐D21‐induced HR. This approach is an efficient way to characterize the roles of genes modulating the hypersensitive defence response and other dominant lesion phenotypes in maize.

Heterosis of leaf and rhizosphere microbiomes in field-grown maize.

Macroorganisms' genotypes shape their phenotypes, which in turn shape the habitat available to potential microbial symbionts. This influence of host genotype on microbiome composition has been demonstrated in many systems; however, most previous studies have either compared unrelated genotypes or delved into molecular mechanisms. As a result, it is currently unclear whether the heritability of host-associated microbiomes follows similar patterns to the heritability of other complex traits. We take a new approach to this question by comparing the microbiomes of diverse maize inbred lines and their F1 hybrid offspring, which we quantified in both rhizosphere and leaves of field-grown plants using 16S-v4 and ITS1 amplicon sequencing. We show that inbred lines and hybrids differ consistently in the composition of bacterial and fungal rhizosphere communities, as well as leaf-associated fungal communities. A wide range of microbiome features display heterosis within individual crosses, consistent with patterns for nonmicrobial maize phenotypes. For leaf microbiomes, these results were supported by the observation that broad-sense heritability in hybrids was substantially higher than narrow-sense heritability. Our results support our hypothesis that at least some heterotic host traits affect microbiome composition in maize.

The Mitochondrial Pentatricopeptide Repeat Protein PPR18 Is Required for the cis-Splicing of nad4 Intron 1 and Essential to Seed Development in Maize.

Pentatricopeptide repeat (PPR) protein comprises a large family, participating in various aspects of organellar RNA metabolism in land plants. There are approximately 600 PPR proteins in maize, but the functions of many PPR proteins remain unknown. In this study, we defined the function of PPR18 in the cis-splicing of nad4 intron 1 in mitochondria and seed development in maize. Loss function of PPR18 seriously impairs embryo and endosperm development, resulting in the empty pericarp (emp) phenotype in maize. PPR18 encodes a mitochondrion-targeted P-type PPR protein with 18 PPR motifs. Transcripts analysis indicated that the splicing of nad4 intron 1 is impaired in the ppr18 mutant, resulting in the absence of nad4 transcript, leading to severely reduced assembly and activity of mitochondrial complex I and dramatically reduced respiration rate. These results demonstrate that PPR18 is required for the cis-splicing of nad4 intron 1 in mitochondria, and critical to complex I assembly and seed development in maize.

Genetic and Physiological Characterization of a Calcium Deficiency Phenotype in Maize.

Calcium (Ca) is an essential plant nutrient, required for signaling, cell wall fortification and growth and development. Calcium deficiency (Ca-deficiency) in maize causes leaf tip rot and a so-called “bull-whipping” or “buggy-whipping” phenotype. Seedlings of the maize line B73 displayed these Ca-deficiency-like symptoms when grown in the greenhouse with excess fertilizer during the winter months, while seedlings of the Mo17 maize line did not display these symptoms under the same conditions. These differential phenotypes could be recapitulated in ‘mini-hydroponic’ systems in the laboratory in which high ammonium, but not nitrate, levels induced the symptoms in B73 but not Mo17 seedlings. Consistent with this phenotype being caused by Ca-deficiency, addition of Ca2+ completely relieved the symptoms. These data suggest that ammonium reduces the seedling’s ability to absorb calcium, which causes the Ca-deficiency phenotype, and that this trait varies among genotypes. A recombinant inbred line (RIL) population derived from a B73 x Mo17 cross was used to map quantitative trait loci (QTL) associated with the Ca-deficiency phenotype. QTL associated with variation in susceptibility to Ca-deficiency were detected on chromosomes 1, 2, 3, 6 which explained between 3.30–9.94% of the observed variation. Several genes predicted to bind or be activated by calcium map to these QTL on chromosome 1, 2, 6. These results describe for the first time the genetics of Ca-deficiency symptoms in maize and in plants in general.

In silico characterization, and expression analysis of rice golden 2-like (OsGLK) members in response to low phosphorous.

The availability of phosphorus (P) affects productivity of rice. Under acidic soil conditions (pH < 5.5), P is rapidly immobilized in the soil. Several transcription factors play an important role in low Pi tolerance response, including MYB family members but their role in acidic soil is yet unknown. In this study, genome wide identification and characterization of golden 2-like (GLK) members belonging to GARP superfamily from rice (OsGLK) led to identification of 46 members distributed over 12 chromosomes. We assigned gene nomenclature, analyzed gene structure and identified mutant orthologs and phenotypes in maize and rice, respectively. On the basis of biological functions three categories viz., (a) two-component response regulator (five members), (b) putative transcription factor (21 members) and (c) phosphate starvation response (8 members) were identified. Phylogenetic analysis revealed a total of nine subgroups with MYB homeodomain-like and MYB CC-type domains conserved across members. Expression profiling of OsGLKs in response to 24 and 48 h of low Pi in four contrasting rice genotypes, revealed significantly higher expression of OsGLK10, OsGLK15, OsGLK22 and OsGLK30 in tolerant genotypes as compared to susceptible genotypes, suggesting their role in Pi starvation tolerance. Meta analyses and cis-regulatory elements (CREs) profiling of OsGLK showed diverse expression pattern in various tissues and organs and also modulation in response to various abiotic and biotic stresses. Our results highlight the versatile role of this diverse and complex GLK family, in particular to abiotic stress. These genes will form the basis of future studies on low Pi tolerance in acidic soils.

Semantic Segmentation of Sorghum Using Hyperspectral Data Identifies Genetic Associations.

This study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle). While many current methods for segmentation focus on separating plant pixels from background, organ-specific segmentation makes it feasible to measure a wider range of plant properties. Manually scored training data for a set of hyperspectral images collected from a sorghum association population was used to train and evaluate a set of supervised classification models. Many algorithms show acceptable accuracy for this classification task. Algorithms trained on sorghum data are able to accurately classify maize leaves and stalks, but fail to accurately classify maize reproductive organs which are not directly equivalent to sorghum panicles. Trait measurements extracted from semantic segmentation of sorghum organs can be used to identify both genes known to be controlling variation in a previously measured phenotypes (e.g., panicle size and plant height) as well as identify signals for genes controlling traits not previously quantified in this population (e.g., stalk/leaf ratio). Organ level semantic segmentation provides opportunities to identify genes controlling variation in a wide range of morphological phenotypes in sorghum, maize, and other related grain crops.