The 3D analysis of plants has become increasingly effective in modeling the relative structure of organs and other traits of interest. In this paper, we introduce a novel pattern-based deep neural network, Pattern-Net, for segmentation of point clouds of wheat. This study is the first to segment the point clouds of wheat into defined organs and to analyse their traits directly in 3D space. Point clouds have no regular grid and thus their segmentation is challenging. Pattern-Net creates a dynamic link among neighbors to seek stable patterns from a 3D point set across several levels of abstraction using the K-nearest neighbor algorithm. To this end, different layers are connected to each other to create complex patterns from the simple ones, strengthen dynamic link propagation, alleviate the vanishing-gradient problem, encourage link reuse and substantially reduce the number of parameters. The proposed deep network is capable of analysing and decomposing unstructured complex point clouds into semantically meaningful parts. Experiments on a wheat dataset verify the effectiveness of our approach for segmentation of wheat in 3D space.
Category: Publication
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A deep learning-integrated micro-CT imageanalysis pipeline for quantifying rice lodgingresistance-related traits
Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architec-
ture is a key aspect of its domestication and a major factor that limits its high productivity. The ideal rice culm
structure, including major_axis_culm, minor axis_culm, and wall thickness_culm, is critical for improving lod-
ging resistance. However, the traditional method of measuring rice culms is destructive, time consuming, and
labor intensive. In this study, we used a high-throughput micro-CT-RGB imaging system and deep learning
(SegNet) to develop a high-throughput micro-CT image analysis pipeline that can extract 24 rice culm morpho-
logical traits and lodging resistance-related traits. When manual and automatic measurements were
compared at the mature stage, the mean absolute percentage errors for major_axis_culm, minor_axis_culm,
and wall_thickness_culm in 104 indica rice accessions were 6.03%, 5.60%, and 9.85%, respectively, and the R2
values were 0.799, 0.818, and 0.623. We also built models of bending stress using culm traits at the mature and
tillering stages, and the R2 values were 0.722 and 0.544, respectively. The modeling results indicated that this
method can quantify lodging resistance nondestructively, even at an early growth stage. In addition, we also
evaluated the relationships of bending stress to shoot dry weight, culm density, and drought-related traits and
found that plants with greater resistance to bending stress had slightly higher biomass, culm density, and
culm area but poorer drought resistance. In conclusion, we developed a deep learning-integrated micro-CT
image analysis pipeline to accurately quantify the phenotypic traits of rice culms in 4.6 min per plant; this
pipeline will assist in future high-throughput screening of large rice populations for lodging resistance. -
Tef: a tiny grain with enormous potential
Tef is a highly nutritious gluten-free Ethiopian cereal with food-feed potential. However, its productivity is affected by lodging, weed infestation, terminal drought, small seed size, and shattering. Following the recent availability of tef genome sequences, we highlight the need to harness the benefits that this underutilised crop offers to improve food security.
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Elucidating drought responsive networks in tef (Eragrostis tef) using phenomic and metabolomic approaches
Drought is a major abiotic stress that limits crop productivity and is driving the need to introduce new tolerant crops with better economic yield. Tef (Eragrostis tef) is a neglected (orphan) Ethiopian warm-season annual gluten-free cereal with high nutritional and health benefits. Further, tef is resilient to environmental challenges such as drought, but the adaptive mechanisms remain poorly understood. In this study, metabolic changes associated with drought response in 11 tef accessions were identified using phenomic and metabolomic approaches under controlled conditions. Computerized image analysis of droughted plants indicated reductions in leaf area and green pigments compared with controls. Metabolite profiling based on flow-infusion electrospray-high-resolution mass spectroscopy (FIE-HRMS) showed drought associated changes in flavonoid, phenylpropanoid biosynthesis, sugar metabolism, valine, leucine and isoleucine biosynthesis, and pentose phosphate pathways. Flavonoid associated metabolites and TCA intermediates were lower in the drought group, whereas most of the stress-responsive amino acids and sugars were elevated. Interestingly, after drought treatment, one accession Enatite (Ent) exhibited a significantly higher plant area than the others, and greater accumulation of flavonoids, amino acids (serine and glycine), sugars (ribose, myo-inositol), and fatty acids. The increased accumulation of these metabolites could explain the increased tolerance to drought in Ent compared with other accessions. This is the first time a non-targeted metabolomics approach has been applied in tef, and our results provide a framework for a better understanding of the tef metabolome during drought stress that will help to identify traits to improve this understudied potential crop.
DOI: 10.1111/ppl.13597
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Untargeted Metabolomic Profiling Reveals Variation in Metabolites Associated with Nutritional Values in Tef Accessions
Tef (Eragrostis tef), is a gluten-free orphan cereal, crop of nutritional and economical significance. Here we used untargeted metabolomics to survey metabolite variation in 14 diverse tef accessions at 15-days post germination. Tef genotypes were classified into four metabolomic groups where variation was linked to flavones and flavonols. Further analysis on white seeded accessions shows variation related to sucrose and important vitamins, nicotinamides (vitamin B3) riboflavin (vitamin B2) and folate (vitamin B9). Coloured seeded accessions showed variation in metabolism related to amino acid and sugars. This study highlights the potential of metabolomics in exploring the nutritional traits in tef.
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Genetic and Methylome Variation in Turkish Brachypodium Distachyon Accessions Differentiate Two Geographically Distinct Subpopulations
Brachypodium distachyon (Brachypodium) is a non-domesticated model grass species that can be used to test if variation in genetic sequence or methylation are linked to environmental differences. To assess this, we collected seeds from 12 sites within five climatically distinct regions of Turkey. Seeds from each region were grown under standardized growth conditions in the UK to preserve methylated sequence variation. At six weeks following germination, leaves were sampled and assessed for genomic and DNA methylation variation. In a follow-up experiment, phenomic approaches were used to describe plant growth and drought responses. Genome sequencing and population structure analysis suggested three ancestral clusters across the Mediterranean, two of which were geographically separated in Turkey into coastal and central subpopulations. Phenotypic analyses showed that the coastal subpopulation tended to exhibit relatively delayed flowering and the central, increased drought tolerance as indicated by reduced yellowing. Genome-wide methylation analyses in GpC, CHG and CHH contexts also showed variation which aligned with the separation into coastal and central subpopulations. The climate niche modelling of both subpopulations showed a significant influence from the “Precipitation in the Driest Quarter” on the central subpopulation and “Temperature of the Coldest Month” on the coastal subpopulation. Our work demonstrates genetic diversity and variation in DNA methylation in Turkish accessions of Brachypodium that may be associated with climate variables and the molecular basis of which will feature in ongoing analyses.
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Direct and accurate feature extraction from 3D point clouds of plants using RANSAC
While point clouds hold promise for measuring the geometrical features of 3D objects, their application to plants remains problematic. Plants are three dimensional (3D) organisms whose morphology is complex, varies from one individual to another and changes over time. Objective measurement of attributes in 3D point cloud domain is increasingly attractive as techniques improve the accuracy and reduce computational time. Analysis of point cloud data, however, is not straightforward, due to its discrete nature, imaging noise and cluttered background. In this paper, we introduce a robust method for the direct analysis of plants of point cloud data. To this end, we generalise the random sample consensus (RANSAC) algorithm for the analysis of 3D point cloud data and then use it to model different plant organs. Since 3D point clouds are obtained from multi-view stereo images, they are often contaminated with a considerable level of noise, distortions and out-of-distribution points. Key to our approach is the use of the RANSAC algorithm on 3D point cloud, making our technique more robust to undesirable outliers. We tested our proposed method on Brassica and grapevine by comparing the estimated measurements extracted from the models with manual ones taken from the actual plants. Our proposed method achieved R 2>0.90 for measured diameters of branches and stems in Brassica while it yielded R 2>0.91 for the measured leaf angles of grapevine and branch angles of Brassica. In all cases, the approach produced stable performance under imaging noise and cluttered background while the conventional methods often failed to work.
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Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding
Background: Selection has dramatically shaped genetic and phenotypic variation in bread wheat. We can assess the genomic basis of historical phenotypic changes, and the potential for future improvement, using experimental populations that attempt to undo selection through the randomizing effects of recombination.
Results: We bred the NIAB Diverse MAGIC multi-parent population comprising over 500 recombinant inbred lines, descended from sixteen historical UK bread wheat varieties released between 1935 and 2004. We sequence the founders’ genes and promoters by capture, and the MAGIC population by low-coverage whole-genome sequencing. We impute 1.1 M high-quality SNPs that are over 99% concordant with array genotypes. Imputation accuracy only marginally improves when including the founders’ genomes as a haplotype reference panel. Despite capturing 73% of global wheat genetic polymorphism, 83% of genes cluster into no more than three haplotypes. We phenotype 47 agronomic traits over 2 years and map 136 genome-wide significant associations, concentrated at 42 genetic loci with large and often pleiotropic effects. Around half of these overlap known quantitative trait loci. Most traits exhibit extensive polygenicity, as revealed by multi-locus shrinkage modelling.
Conclusions: Our results are consistent with a gene pool of low haplotypic diversity, containing few novel loci of large effect. Most past, and projected future, phenotypic changes arising from existing variation involve fine-scale shuffling of a few haplotypes to recombine dozens of polygenic alleles of small effect. Moreover, extensive pleiotropy means selection on one trait will have unintended consequences, exemplified by the negative trade-off between yield and protein content, unless selection and recombination can break unfavorable trait-trait associations.
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Ectopic expression of Triticum polonicum VRT-A2 underlies elongated glumes and grains in hexaploid wheat in a dosage-dependent manner
Flower development is an important determinant of grain yield in crops. In wheat (Triticum spp.), natural variation for the size of spikelet and floral organs is particularly evident in Triticum turgidum ssp. polonicum (also termed Triticum polonicum), a tetraploid subspecies of wheat with long glumes, lemmas, and grains. Using map-based cloning, we identified VEGETATIVE TO REPRODUCTIVE TRANSITION 2 (VRT2), which encodes a MADS-box transcription factor belonging to the SHORT VEGETATIVE PHASE family, as the gene underlying the T. polonicum long-glume (P1) locus. The causal P1 mutation is a sequence rearrangement in intron-1 that results in ectopic expression of the T. polonicum VRT-A2 allele. Based on allelic variation studies, we propose that the intron-1 mutation in VRT-A2 is the unique T. polonicum subspecies-defining polymorphism, which was later introduced into hexaploid wheat via natural hybridizations. Near-isogenic lines differing for the P1 locus revealed a gradient effect of P1 across spikelets and within florets. Transgenic lines of hexaploid wheat carrying the T. polonicum VRT-A2 allele show that expression levels of VRT-A2 are highly correlated with spike, glume, grain, and floral organ length. These results highlight how changes in expression profiles, through variation in cis-regulation, can affect agronomic traits in a dosage-dependent manner in polyploid crops.
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Development of an Image Analysis Pipeline to Estimate Sphagnum Colony Density in the Field
Sphagnum peatmosses play an important part in water table management of many peatland ecosystems. Keeping the ecosystem saturated, they slow the breakdown of organic matter and release of greenhouse gases, facilitating peatland’s function as a carbon sink rather than a carbon source. Although peatland monitoring and restoration programs have increased recently, there are few tools to quantify traits that Sphagnum species display in their ecosystems. Colony density is often described as an important determinant in the establishment and performance in Sphagnum but detailed evidence for this is limited. In this study, we describe an image analysis pipeline that accurately annotates Sphagnum capitula and estimates plant density using open access computer vision packages. The pipeline was validated using images of different Sphagnum species growing in different habitats, taken on different days and with different smartphones. The developed pipeline achieves high accuracy scores, and we demonstrate its utility by estimating colony densities in the field and detecting intra and inter-specific colony densities and their relationship with habitat. This tool will enable ecologists and conservationists to rapidly acquire accurate estimates of Sphagnum density in the field without the need of specialised equipment.